Embedding People of Informatics¶
Here, embed individuals in different dimensions, as a form of dimensionality reduction. The following models were used to embed individuals:
- tf-idf
- metadata
- metadata + pdf
- topic models
- topic model derived from metadata
- topic model derived using documents in DBLP
Methodology¶
- Derive the models (for topic models, that is already derived in notebook on topicModelling.
- Map individuals to the publications that they have published. (a bipartite graph between publications and individuals)
- FOR each individual, using the publications for each individual, derive the embedding in the new space.
- Calculate metrics such as similarity between individuals.
Setup¶
In [1]:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use(['seaborn-poster'])
%matplotlib inline
In [2]:
import networkx as nx
from infnet_helper import *
import network_artist as artist
from embedding_helper import *
# For dynamic reloading of modules
%load_ext autoreload
%autoreload
2018-04-11 22:27:50,311 : INFO : 'pattern' package not found; tag filters are not available for English
In [3]:
# Load publication mapping:
# This mapping contains all the publicatitons that an individual have published.
# it does not filter out those that are only relevant to each time period
# The function:gen_toks in helper_embedding does that by concatenating publications that
# are relevant to the time period under consideration in lookup_combined_toks
# (which is filtered according to the year)
df_pubmapping = get_poinf_pub_mapping()
In [4]:
def to_list(l, target_length):
"""
List of tuples...
"""
out = np.zeros(target_length, dtype=np.float32)
for (i, v) in l:
out[i] = v
return out
Simple embedding using TF-IDF¶
- Create TF-IDF for each terms
- Using the mapping for
poinf-> publicationscreated, create list of tokens for each indivudal - As in information retrieval, find individuals who have are most similar. This is done using
cosine similarity
using metadata from 6yr¶
Import metadata corpus:¶
In [5]:
lookup_combined_toks = prepare_toks(2012,2017, with_pdf2txt=False)
In [6]:
# Base model for collab network
collabnet = pd.read_csv(
'../../data/data_schoolofinf/poinf_collabgraph_2012-2017.txt',
sep='\n',
names=['id'])
adj_mat = np.load('../../data/data_schoolofinf/mat/infnet6yrs-adj-mat.order6yr.pkl')
In [7]:
# Gather publications for each individuals
model = collabnet.join(df_pubmapping, how='left',on='id')
# generate tokens
model['toks'] = model['pub_ids'].apply(
lambda a: gen_toks(a, lookup_combined_toks))
In [8]:
dict_tm = Dictionary.load('../../data/data_schoolofinf/corpora/dictionary.less.meta')
# Use the tfidf model from gensim:
tfidf_metada = TfidfModel(dictionary=dict_tm, normalize=True)
2018-04-11 22:27:53,517 : INFO : loading Dictionary object from ../../data/data_schoolofinf/corpora/dictionary.less.meta 2018-04-11 22:27:53,554 : INFO : loaded ../../data/data_schoolofinf/corpora/dictionary.less.meta
Generate tfidf scores for each individual¶
Using the concatenation of publications (tokens) derived for each individual, we calculate the tfidf score for that individual.
This score is then used to calculate similarities.
In [9]:
model['tfidf'] = model['toks'].apply(
lambda x: tfidf_metada[dict_tm.doc2bow(x)] if len(x) else None
)
Similarity matrix¶
In [10]:
cosim = compare_researchers(model.tfidf.tolist(), len(dict_tm))
In [11]:
# finding the best threshold
thresholds, edges, distances, closest_edges, best_threshold, lowest_avg_distances, best_j_dist_epoch, best_j_dist_threshold = \
find_best_threshold(adj_mat, cosim, num_iter=10000, step_size=0.001)
2018-04-11 22:32:07,902 : INFO : Number of ground_truth_edges: 361.0 2018-04-11 22:32:07,903 : INFO : binary edges: True 2018-04-11 22:32:07,911 : INFO : epoch 0: threshold: 0.001 avg_dist: 0.979 num_edges: 16834 2018-04-11 22:32:07,923 : INFO : epoch 1: threshold: 0.002 avg_dist: 0.979 num_edges: 16833 2018-04-11 22:32:07,930 : INFO : epoch 2: threshold: 0.003 avg_dist: 0.979 num_edges: 16833 2018-04-11 22:32:07,937 : INFO : epoch 3: threshold: 0.004 avg_dist: 0.979 num_edges: 16829 2018-04-11 22:32:07,944 : INFO : epoch 4: threshold: 0.005 avg_dist: 0.979 num_edges: 16825 2018-04-11 22:32:07,951 : INFO : epoch 5: threshold: 0.006 avg_dist: 0.979 num_edges: 16825 2018-04-11 22:32:07,961 : INFO : epoch 6: threshold: 0.007 avg_dist: 0.979 num_edges: 16820 2018-04-11 22:32:07,973 : INFO : epoch 7: threshold: 0.008 avg_dist: 0.979 num_edges: 16814 2018-04-11 22:32:07,981 : INFO : epoch 8: threshold: 0.009 avg_dist: 0.979 num_edges: 16807 2018-04-11 22:32:07,990 : INFO : epoch 9: threshold: 0.010 avg_dist: 0.979 num_edges: 16800 2018-04-11 22:32:07,997 : INFO : epoch 10: threshold: 0.011 avg_dist: 0.979 num_edges: 16794 2018-04-11 22:32:08,007 : INFO : epoch 11: threshold: 0.012 avg_dist: 0.979 num_edges: 16786 2018-04-11 22:32:08,014 : INFO : epoch 12: threshold: 0.013 avg_dist: 0.979 num_edges: 16777 2018-04-11 22:32:08,023 : INFO : epoch 13: threshold: 0.014 avg_dist: 0.979 num_edges: 16766 2018-04-11 22:32:08,034 : INFO : epoch 14: threshold: 0.015 avg_dist: 0.979 num_edges: 16751 2018-04-11 22:32:08,042 : INFO : epoch 15: threshold: 0.016 avg_dist: 0.979 num_edges: 16733 2018-04-11 22:32:08,051 : INFO : epoch 16: threshold: 0.017 avg_dist: 0.978 num_edges: 16714 2018-04-11 22:32:08,058 : INFO : epoch 17: threshold: 0.018 avg_dist: 0.978 num_edges: 16684 2018-04-11 22:32:08,068 : INFO : epoch 18: threshold: 0.019 avg_dist: 0.978 num_edges: 16645 2018-04-11 22:32:08,079 : INFO : epoch 19: threshold: 0.020 avg_dist: 0.978 num_edges: 16619 2018-04-11 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22:32:15,868 : INFO : epoch 946: threshold: 0.947 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,881 : INFO : epoch 947: threshold: 0.948 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,888 : INFO : epoch 948: threshold: 0.949 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,895 : INFO : epoch 949: threshold: 0.950 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,902 : INFO : epoch 950: threshold: 0.951 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,908 : INFO : epoch 951: threshold: 0.952 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,916 : INFO : epoch 952: threshold: 0.953 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,928 : INFO : epoch 953: threshold: 0.954 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,935 : INFO : epoch 954: threshold: 0.955 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,942 : INFO : epoch 955: threshold: 0.956 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,949 : INFO : epoch 956: threshold: 0.957 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,956 : INFO : epoch 957: threshold: 0.958 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,963 : INFO : epoch 958: threshold: 0.959 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,970 : INFO : epoch 959: threshold: 0.960 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,977 : INFO : epoch 960: threshold: 0.961 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,987 : INFO : epoch 961: threshold: 0.962 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:15,994 : INFO : epoch 962: threshold: 0.963 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,002 : INFO : epoch 963: threshold: 0.964 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,009 : INFO : epoch 964: threshold: 0.965 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,021 : INFO : epoch 965: threshold: 0.966 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,028 : INFO : epoch 966: threshold: 0.967 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,035 : INFO : epoch 967: threshold: 0.968 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,042 : INFO : epoch 968: threshold: 0.969 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,050 : INFO : epoch 969: threshold: 0.970 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,057 : INFO : epoch 970: threshold: 0.971 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,066 : INFO : epoch 971: threshold: 0.972 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,073 : INFO : epoch 972: threshold: 0.973 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,080 : INFO : epoch 973: threshold: 0.974 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,087 : INFO : epoch 974: threshold: 0.975 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,093 : INFO : epoch 975: threshold: 0.976 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,104 : INFO : epoch 976: threshold: 0.977 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,112 : INFO : epoch 977: threshold: 0.978 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,120 : INFO : epoch 978: threshold: 0.979 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,132 : INFO : epoch 979: threshold: 0.980 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,139 : INFO : epoch 980: threshold: 0.981 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,149 : INFO : epoch 981: threshold: 0.982 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,157 : INFO : epoch 982: threshold: 0.983 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,168 : INFO : epoch 983: threshold: 0.984 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,174 : INFO : epoch 984: threshold: 0.985 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,182 : INFO : epoch 985: threshold: 0.986 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,189 : INFO : epoch 986: threshold: 0.987 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,196 : INFO : epoch 987: threshold: 0.988 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,202 : INFO : epoch 988: threshold: 0.989 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,209 : INFO : epoch 989: threshold: 0.990 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,215 : INFO : epoch 990: threshold: 0.991 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,222 : INFO : epoch 991: threshold: 0.992 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,232 : INFO : epoch 992: threshold: 0.993 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,241 : INFO : epoch 993: threshold: 0.994 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,247 : INFO : epoch 994: threshold: 0.995 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,254 : INFO : epoch 995: threshold: 0.996 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,260 : INFO : epoch 996: threshold: 0.997 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,267 : INFO : epoch 997: threshold: 0.998 avg_dist: 0.995 num_edges: 1 2018-04-11 22:32:16,274 : INFO : epoch 998: threshold: 0.999 avg_dist: 0.995 num_edges: 1
threshold: 0.319 dist 0.542208 (337, 0.338) edges 364/361.0 (318)
threshold: 0.319 dist 0.539511 (337, 0.338) edges 364/360.0 (318)
In [12]:
f = threshold_plot(
thresholds,
distances,
edges,
best_threshold,
closest_edges,
best_j_dist_threshold,
lowest_avg_distances,
np.sum(adj_mat)//2,
cosim
)
In [13]:
f.savefig('IMG/tfidf_thresholding.png', format='png', bbox_inches='tight')
Generate network¶
In [14]:
model_adj_mat = set_edges(cosim, best_threshold,binary=True)
2018-04-11 22:32:18,535 : INFO : dimension: 184, 184
In [15]:
# visualise matrix
f = plt.figure(figsize=(15, 5))
# ORIGINAL COLALB NET
ax1 = f.add_subplot(131)
ax1.axis('off')
sns.heatmap(adj_mat, cbar=False, square=True)
ax1.set_title('infnet20yr\n(#Edges={})'.format(int(np.sum(adj_mat) // 2)))
# COSIM
ax2 = f.add_subplot(132)
ax2.axis('off')
sns.heatmap(
cosim,
cbar=False,
square=True,
ax=ax2,
vmax=1,
vmin=0,
robust=True,
cmap='gnuplot2')
ax2.set_title('Cosine similairty\nbetween researchers')
# FINAL :
ax3 = f.add_subplot(133)
ax3.axis('off')
sns.heatmap(model_adj_mat, cbar=False, square=True, ax=ax3)
ax3.set_title('tfidf\n($\epsilon$={:.3f}, #Edges={})'.format(
best_threshold,
int(np.sum(model_adj_mat) // 2)))
f.tight_layout()
In [16]:
g_tfidf = adj_mat_to_graph(model_adj_mat, collabnet.id.tolist())
In [17]:
print(nx.info(g_tfidf))
Name: Type: Graph Number of nodes: 166 Number of edges: 364 Average degree: 4.3855
In [18]:
artist.draw_default_layout(
g_tfidf,
lookup_poinf=get_lookup_poinf(),
file_prefix='tfidf_6yr',
SAVE_GRAPHS=True)
2018-04-11 22:32:19,582 : INFO : SAVE_GRAPHS: True
In [19]:
artist.draw_circular_layout(
g_tfidf,
get_lookup_poinf(),
file_prefix='tfidf_6yr',
SAVE_GRAPHS=True)
2018-04-11 22:32:22,398 : INFO : SAVE_GRAPHS: True
Descriptive statistics¶
In [22]:
nx.draw(
g_tfidf,
pos=nx.spring_layout(g_tfidf),
node_size=30,
node_color=artist.color_by_inst(g_tfidf, get_lookup_poinf()))
In [23]:
gccs = generateGCC(g_tfidf)
component 1: 85.54% component 2: 4.22% component 3: 2.41% component 4: 2.41% component 5: 1.81% component 6: 1.20% component 7: 1.20% component 8: 1.20%
In [24]:
c_coeff,avg=clustering_coeff(g_tfidf)
print(avg)
0.49925783720964445
In [25]:
# transitivity
nx.transitivity(g_tfidf)
Out[25]:
0.4975822050290135
In [26]:
x = nx.degree_histogram(g_tfidf)
print(x[0]+x[1])
28
topic-collaboration networks¶
In [27]:
# filter our lookup table according to the tokens
lookup_combined_toks = prepare_toks(top=1997, bottom=2017)
Deriving topicnet-20yr¶
In [28]:
# Load Collaboration Network
collabnet = pd.read_csv('../../data/data_schoolofinf/poinf_collabgraph_1997-2017.txt',
sep='\n',
names=['id'])
adj_mat = np.load('../../data/data_schoolofinf/mat/infnet20yrs-adj-mat.pkl')
In [29]:
# Gather the publications for each individual:
topicnet = collabnet.join(df_pubmapping, how='left', on='id')
In [30]:
# Load Topic Model
tm = models.LdaModel.load('../../topicModel/src/tmfull_meta/res/best_ldamodel')
print((tm.minimum_probability))
dict_tm = Dictionary.load('../../data/data_schoolofinf/corpora/dictionary.meta')
# Generate tokens for each publication:
topicnet['toks'] = topicnet['pub_ids'].apply(lambda a: gen_toks(a, lookup_combined_toks))
2018-04-11 22:35:42,975 : INFO : loading LdaModel object from ../../topicModel/src/tmfull_meta/res/best_ldamodel 2018-04-11 22:35:42,997 : INFO : loading expElogbeta from ../../topicModel/src/tmfull_meta/res/best_ldamodel.expElogbeta.npy with mmap=None 2018-04-11 22:35:43,024 : INFO : setting ignored attribute dispatcher to None 2018-04-11 22:35:43,025 : INFO : setting ignored attribute state to None 2018-04-11 22:35:43,025 : INFO : setting ignored attribute id2word to None 2018-04-11 22:35:43,028 : INFO : loaded ../../topicModel/src/tmfull_meta/res/best_ldamodel 2018-04-11 22:35:43,029 : INFO : loading LdaModel object from ../../topicModel/src/tmfull_meta/res/best_ldamodel.state 2018-04-11 22:35:43,066 : INFO : loaded ../../topicModel/src/tmfull_meta/res/best_ldamodel.state 2018-04-11 22:35:43,079 : INFO : loading Dictionary object from ../../data/data_schoolofinf/corpora/dictionary.meta 2018-04-11 22:35:43,095 : INFO : loaded ../../data/data_schoolofinf/corpora/dictionary.meta
0.01
In [31]:
# use TM for infernece on the model
topicnet['topic_probs'] = topicnet['toks'].apply(lambda a: tm.get_document_topics(dict_tm.doc2bow(a))
if len(a) else None)
In [32]:
# Generate cosine similarity between individuals:
cosim = compare_researchers(topicnet.topic_probs.tolist(), tm.num_topics)
In [10]:
# find the best threshold:
thresholds, edges, distances, closest_edges, best_threshold, lowest_avg_distances, best_j_dist_epoch, best_j_dist_threshold = \
find_best_threshold(adj_mat, cosim, num_iter=10000, step_size=0.001)
2018-04-06 20:02:46,313 : INFO : Number of ground_truth_edges: 471.0 2018-04-06 20:02:46,314 : INFO : binary edges: True 2018-04-06 20:02:46,322 : INFO : epoch 0: threshold: 0.001 avg_dist: 0.975 num_edges: 18504 2018-04-06 20:02:46,329 : INFO : epoch 1: threshold: 0.002 avg_dist: 0.974 num_edges: 18369 2018-04-06 20:02:46,336 : INFO : epoch 2: threshold: 0.003 avg_dist: 0.974 num_edges: 18231 2018-04-06 20:02:46,343 : INFO : epoch 3: threshold: 0.004 avg_dist: 0.974 num_edges: 18094 2018-04-06 20:02:46,350 : INFO : epoch 4: threshold: 0.005 avg_dist: 0.974 num_edges: 17949 2018-04-06 20:02:46,357 : INFO : epoch 5: threshold: 0.006 avg_dist: 0.974 num_edges: 17829 2018-04-06 20:02:46,363 : INFO : epoch 6: threshold: 0.007 avg_dist: 0.973 num_edges: 17690 2018-04-06 20:02:46,370 : INFO : epoch 7: threshold: 0.008 avg_dist: 0.973 num_edges: 17542 2018-04-06 20:02:46,376 : INFO : epoch 8: threshold: 0.009 avg_dist: 0.973 num_edges: 17401 2018-04-06 20:02:46,383 : INFO : epoch 9: threshold: 0.010 avg_dist: 0.973 num_edges: 17250 2018-04-06 20:02:46,389 : INFO : epoch 10: threshold: 0.011 avg_dist: 0.972 num_edges: 17108 2018-04-06 20:02:46,396 : INFO : epoch 11: threshold: 0.012 avg_dist: 0.972 num_edges: 16982 2018-04-06 20:02:46,403 : INFO : epoch 12: threshold: 0.013 avg_dist: 0.972 num_edges: 16839 2018-04-06 20:02:46,409 : INFO : epoch 13: threshold: 0.014 avg_dist: 0.972 num_edges: 16715 2018-04-06 20:02:46,416 : INFO : epoch 14: threshold: 0.015 avg_dist: 0.972 num_edges: 16590 2018-04-06 20:02:46,422 : INFO : epoch 15: threshold: 0.016 avg_dist: 0.971 num_edges: 16453 2018-04-06 20:02:46,429 : INFO : epoch 16: threshold: 0.017 avg_dist: 0.971 num_edges: 16319 2018-04-06 20:02:46,435 : INFO : epoch 17: threshold: 0.018 avg_dist: 0.971 num_edges: 16201 2018-04-06 20:02:46,442 : INFO : epoch 18: threshold: 0.019 avg_dist: 0.970 num_edges: 16072 2018-04-06 20:02:46,449 : INFO : epoch 19: threshold: 0.020 avg_dist: 0.970 num_edges: 15957 2018-04-06 20:02:46,455 : INFO : epoch 20: threshold: 0.021 avg_dist: 0.970 num_edges: 15840 2018-04-06 20:02:46,462 : INFO : epoch 21: threshold: 0.022 avg_dist: 0.970 num_edges: 15713 2018-04-06 20:02:46,469 : INFO : epoch 22: threshold: 0.023 avg_dist: 0.969 num_edges: 15602 2018-04-06 20:02:46,476 : INFO : epoch 23: threshold: 0.024 avg_dist: 0.969 num_edges: 15476 2018-04-06 20:02:46,482 : INFO : epoch 24: threshold: 0.025 avg_dist: 0.969 num_edges: 15340 2018-04-06 20:02:46,489 : INFO : epoch 25: threshold: 0.026 avg_dist: 0.969 num_edges: 15219 2018-04-06 20:02:46,495 : INFO : epoch 26: threshold: 0.027 avg_dist: 0.968 num_edges: 15116 2018-04-06 20:02:46,502 : INFO : epoch 27: threshold: 0.028 avg_dist: 0.968 num_edges: 15007 2018-04-06 20:02:46,509 : INFO : epoch 28: threshold: 0.029 avg_dist: 0.968 num_edges: 14897 2018-04-06 20:02:46,515 : INFO : epoch 29: threshold: 0.030 avg_dist: 0.968 num_edges: 14783 2018-04-06 20:02:46,522 : INFO : epoch 30: threshold: 0.031 avg_dist: 0.967 num_edges: 14691 2018-04-06 20:02:46,529 : INFO : epoch 31: threshold: 0.032 avg_dist: 0.967 num_edges: 14566 2018-04-06 20:02:46,536 : INFO : epoch 32: threshold: 0.033 avg_dist: 0.967 num_edges: 14455 2018-04-06 20:02:46,542 : INFO : epoch 33: threshold: 0.034 avg_dist: 0.966 num_edges: 14352 2018-04-06 20:02:46,549 : INFO : epoch 34: threshold: 0.035 avg_dist: 0.966 num_edges: 14253 2018-04-06 20:02:46,556 : INFO : epoch 35: threshold: 0.036 avg_dist: 0.966 num_edges: 14154 2018-04-06 20:02:46,563 : INFO : epoch 36: threshold: 0.037 avg_dist: 0.965 num_edges: 14068 2018-04-06 20:02:46,569 : INFO : epoch 37: threshold: 0.038 avg_dist: 0.965 num_edges: 13967 2018-04-06 20:02:46,576 : INFO : epoch 38: threshold: 0.039 avg_dist: 0.965 num_edges: 13868 2018-04-06 20:02:46,583 : INFO : epoch 39: threshold: 0.040 avg_dist: 0.965 num_edges: 13771 2018-04-06 20:02:46,590 : INFO : epoch 40: threshold: 0.041 avg_dist: 0.964 num_edges: 13679 2018-04-06 20:02:46,597 : INFO : epoch 41: threshold: 0.042 avg_dist: 0.964 num_edges: 13598 2018-04-06 20:02:46,603 : INFO : epoch 42: threshold: 0.043 avg_dist: 0.964 num_edges: 13503 2018-04-06 20:02:46,610 : INFO : epoch 43: threshold: 0.044 avg_dist: 0.964 num_edges: 13407 2018-04-06 20:02:46,617 : INFO : epoch 44: threshold: 0.045 avg_dist: 0.963 num_edges: 13300 2018-04-06 20:02:46,624 : INFO : epoch 45: threshold: 0.046 avg_dist: 0.963 num_edges: 13189 2018-04-06 20:02:46,631 : INFO : epoch 46: threshold: 0.047 avg_dist: 0.962 num_edges: 13087 2018-04-06 20:02:46,637 : INFO : epoch 47: threshold: 0.048 avg_dist: 0.962 num_edges: 12986 2018-04-06 20:02:46,644 : INFO : epoch 48: threshold: 0.049 avg_dist: 0.962 num_edges: 12884 2018-04-06 20:02:46,651 : INFO : epoch 49: threshold: 0.050 avg_dist: 0.961 num_edges: 12793 2018-04-06 20:02:46,658 : INFO : epoch 50: threshold: 0.051 avg_dist: 0.961 num_edges: 12691 2018-04-06 20:02:46,664 : INFO : epoch 51: threshold: 0.052 avg_dist: 0.961 num_edges: 12587 2018-04-06 20:02:46,671 : INFO : epoch 52: threshold: 0.053 avg_dist: 0.960 num_edges: 12511 2018-04-06 20:02:46,678 : INFO : epoch 53: threshold: 0.054 avg_dist: 0.960 num_edges: 12434 2018-04-06 20:02:46,685 : INFO : epoch 54: threshold: 0.055 avg_dist: 0.960 num_edges: 12352 2018-04-06 20:02:46,692 : INFO : epoch 55: threshold: 0.056 avg_dist: 0.960 num_edges: 12253 2018-04-06 20:02:46,699 : INFO : epoch 56: threshold: 0.057 avg_dist: 0.959 num_edges: 12161 2018-04-06 20:02:46,706 : INFO : epoch 57: threshold: 0.058 avg_dist: 0.959 num_edges: 12074 2018-04-06 20:02:46,713 : INFO : epoch 58: threshold: 0.059 avg_dist: 0.958 num_edges: 11977 2018-04-06 20:02:46,719 : INFO : epoch 59: threshold: 0.060 avg_dist: 0.958 num_edges: 11889 2018-04-06 20:02:46,726 : INFO : epoch 60: threshold: 0.061 avg_dist: 0.958 num_edges: 11805 2018-04-06 20:02:46,733 : INFO : epoch 61: threshold: 0.062 avg_dist: 0.957 num_edges: 11719 2018-04-06 20:02:46,739 : INFO : epoch 62: threshold: 0.063 avg_dist: 0.957 num_edges: 11644 2018-04-06 20:02:46,746 : INFO : epoch 63: threshold: 0.064 avg_dist: 0.957 num_edges: 11554 2018-04-06 20:02:46,753 : INFO : epoch 64: threshold: 0.065 avg_dist: 0.956 num_edges: 11459 2018-04-06 20:02:46,759 : INFO : epoch 65: threshold: 0.066 avg_dist: 0.956 num_edges: 11390 2018-04-06 20:02:46,766 : INFO : epoch 66: threshold: 0.067 avg_dist: 0.956 num_edges: 11309 2018-04-06 20:02:46,773 : INFO : epoch 67: threshold: 0.068 avg_dist: 0.955 num_edges: 11238 2018-04-06 20:02:46,780 : INFO : epoch 68: threshold: 0.069 avg_dist: 0.955 num_edges: 11159 2018-04-06 20:02:46,787 : INFO : epoch 69: threshold: 0.070 avg_dist: 0.955 num_edges: 11075 2018-04-06 20:02:46,793 : INFO : epoch 70: threshold: 0.071 avg_dist: 0.954 num_edges: 11000 2018-04-06 20:02:46,800 : INFO : epoch 71: threshold: 0.072 avg_dist: 0.954 num_edges: 10931 2018-04-06 20:02:46,807 : INFO : epoch 72: threshold: 0.073 avg_dist: 0.954 num_edges: 10860 2018-04-06 20:02:46,814 : INFO : epoch 73: threshold: 0.074 avg_dist: 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epoch 697: threshold: 0.698 avg_dist: 0.797 num_edges: 984 2018-04-06 20:02:51,136 : INFO : epoch 698: threshold: 0.699 avg_dist: 0.797 num_edges: 979 2018-04-06 20:02:51,145 : INFO : epoch 699: threshold: 0.700 avg_dist: 0.797 num_edges: 977 2018-04-06 20:02:51,155 : INFO : epoch 700: threshold: 0.701 avg_dist: 0.796 num_edges: 971 2018-04-06 20:02:51,164 : INFO : epoch 701: threshold: 0.702 avg_dist: 0.797 num_edges: 969 2018-04-06 20:02:51,173 : INFO : epoch 702: threshold: 0.703 avg_dist: 0.796 num_edges: 961 2018-04-06 20:02:51,181 : INFO : epoch 703: threshold: 0.704 avg_dist: 0.795 num_edges: 954 2018-04-06 20:02:51,190 : INFO : epoch 704: threshold: 0.705 avg_dist: 0.795 num_edges: 952 2018-04-06 20:02:51,197 : INFO : epoch 705: threshold: 0.706 avg_dist: 0.795 num_edges: 951 2018-04-06 20:02:51,204 : INFO : epoch 706: threshold: 0.707 avg_dist: 0.795 num_edges: 949 2018-04-06 20:02:51,211 : INFO : epoch 707: threshold: 0.708 avg_dist: 0.795 num_edges: 946 2018-04-06 20:02:51,217 : INFO : epoch 708: threshold: 0.709 avg_dist: 0.792 num_edges: 943 2018-04-06 20:02:51,224 : INFO : epoch 709: threshold: 0.710 avg_dist: 0.793 num_edges: 937 2018-04-06 20:02:51,230 : INFO : epoch 710: threshold: 0.711 avg_dist: 0.792 num_edges: 933 2018-04-06 20:02:51,237 : INFO : epoch 711: threshold: 0.712 avg_dist: 0.792 num_edges: 931 2018-04-06 20:02:51,243 : INFO : epoch 712: threshold: 0.713 avg_dist: 0.791 num_edges: 929 2018-04-06 20:02:51,250 : INFO : epoch 713: threshold: 0.714 avg_dist: 0.791 num_edges: 924 2018-04-06 20:02:51,256 : INFO : epoch 714: threshold: 0.715 avg_dist: 0.793 num_edges: 919 2018-04-06 20:02:51,263 : INFO : epoch 715: threshold: 0.716 avg_dist: 0.792 num_edges: 915 2018-04-06 20:02:51,270 : INFO : epoch 716: threshold: 0.717 avg_dist: 0.792 num_edges: 910 2018-04-06 20:02:51,276 : INFO : epoch 717: threshold: 0.718 avg_dist: 0.792 num_edges: 906 2018-04-06 20:02:51,283 : INFO : epoch 718: threshold: 0.719 avg_dist: 0.792 num_edges: 898 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epoch 827: threshold: 0.828 avg_dist: 0.809 num_edges: 505 2018-04-06 20:02:52,015 : INFO : epoch 828: threshold: 0.829 avg_dist: 0.809 num_edges: 500 2018-04-06 20:02:52,022 : INFO : epoch 829: threshold: 0.830 avg_dist: 0.814 num_edges: 496 2018-04-06 20:02:52,028 : INFO : epoch 830: threshold: 0.831 avg_dist: 0.814 num_edges: 493 2018-04-06 20:02:52,035 : INFO : epoch 831: threshold: 0.832 avg_dist: 0.813 num_edges: 491 2018-04-06 20:02:52,041 : INFO : epoch 832: threshold: 0.833 avg_dist: 0.814 num_edges: 486 2018-04-06 20:02:52,048 : INFO : epoch 833: threshold: 0.834 avg_dist: 0.814 num_edges: 484 2018-04-06 20:02:52,055 : INFO : epoch 834: threshold: 0.835 avg_dist: 0.814 num_edges: 483 2018-04-06 20:02:52,061 : INFO : epoch 835: threshold: 0.836 avg_dist: 0.813 num_edges: 476 2018-04-06 20:02:52,068 : INFO : epoch 836: threshold: 0.837 avg_dist: 0.813 num_edges: 473 2018-04-06 20:02:52,075 : INFO : epoch 837: threshold: 0.838 avg_dist: 0.813 num_edges: 470 2018-04-06 20:02:52,081 : INFO : epoch 838: threshold: 0.839 avg_dist: 0.813 num_edges: 468 2018-04-06 20:02:52,088 : INFO : epoch 839: threshold: 0.840 avg_dist: 0.815 num_edges: 465 2018-04-06 20:02:52,094 : INFO : epoch 840: threshold: 0.841 avg_dist: 0.815 num_edges: 463 2018-04-06 20:02:52,101 : INFO : epoch 841: threshold: 0.842 avg_dist: 0.815 num_edges: 461 2018-04-06 20:02:52,107 : INFO : epoch 842: threshold: 0.843 avg_dist: 0.815 num_edges: 459 2018-04-06 20:02:52,114 : INFO : epoch 843: threshold: 0.844 avg_dist: 0.815 num_edges: 452 2018-04-06 20:02:52,120 : INFO : epoch 844: threshold: 0.845 avg_dist: 0.815 num_edges: 451 2018-04-06 20:02:52,127 : INFO : epoch 845: threshold: 0.846 avg_dist: 0.818 num_edges: 447 2018-04-06 20:02:52,133 : INFO : epoch 846: threshold: 0.847 avg_dist: 0.817 num_edges: 445 2018-04-06 20:02:52,140 : INFO : epoch 847: threshold: 0.848 avg_dist: 0.818 num_edges: 439 2018-04-06 20:02:52,146 : INFO : epoch 848: threshold: 0.849 avg_dist: 0.817 num_edges: 437 2018-04-06 20:02:52,153 : INFO : epoch 849: threshold: 0.850 avg_dist: 0.818 num_edges: 432 2018-04-06 20:02:52,159 : INFO : epoch 850: threshold: 0.851 avg_dist: 0.817 num_edges: 430 2018-04-06 20:02:52,166 : INFO : epoch 851: threshold: 0.852 avg_dist: 0.818 num_edges: 427 2018-04-06 20:02:52,172 : INFO : epoch 852: threshold: 0.853 avg_dist: 0.818 num_edges: 424 2018-04-06 20:02:52,179 : INFO : epoch 853: threshold: 0.854 avg_dist: 0.818 num_edges: 424 2018-04-06 20:02:52,185 : INFO : epoch 854: threshold: 0.855 avg_dist: 0.818 num_edges: 422 2018-04-06 20:02:52,192 : INFO : epoch 855: threshold: 0.856 avg_dist: 0.818 num_edges: 420 2018-04-06 20:02:52,198 : INFO : epoch 856: threshold: 0.857 avg_dist: 0.818 num_edges: 417 2018-04-06 20:02:52,205 : INFO : epoch 857: threshold: 0.858 avg_dist: 0.819 num_edges: 412 2018-04-06 20:02:52,211 : INFO : epoch 858: threshold: 0.859 avg_dist: 0.818 num_edges: 410 2018-04-06 20:02:52,218 : INFO : epoch 859: threshold: 0.860 avg_dist: 0.819 num_edges: 408 2018-04-06 20:02:52,224 : INFO : epoch 860: threshold: 0.861 avg_dist: 0.818 num_edges: 405 2018-04-06 20:02:52,231 : INFO : epoch 861: threshold: 0.862 avg_dist: 0.818 num_edges: 402 2018-04-06 20:02:52,238 : INFO : epoch 862: threshold: 0.863 avg_dist: 0.818 num_edges: 397 2018-04-06 20:02:52,244 : INFO : epoch 863: threshold: 0.864 avg_dist: 0.818 num_edges: 395 2018-04-06 20:02:52,251 : INFO : epoch 864: threshold: 0.865 avg_dist: 0.818 num_edges: 393 2018-04-06 20:02:52,257 : INFO : epoch 865: threshold: 0.866 avg_dist: 0.819 num_edges: 387 2018-04-06 20:02:52,264 : INFO : epoch 866: threshold: 0.867 avg_dist: 0.822 num_edges: 384 2018-04-06 20:02:52,271 : INFO : epoch 867: threshold: 0.868 avg_dist: 0.824 num_edges: 380 2018-04-06 20:02:52,277 : INFO : epoch 868: threshold: 0.869 avg_dist: 0.823 num_edges: 378 2018-04-06 20:02:52,284 : INFO : epoch 869: threshold: 0.870 avg_dist: 0.823 num_edges: 375 2018-04-06 20:02:52,290 : INFO : epoch 870: threshold: 0.871 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threshold: 0.882 avg_dist: 0.826 num_edges: 336 2018-04-06 20:02:52,369 : INFO : epoch 882: threshold: 0.883 avg_dist: 0.825 num_edges: 334 2018-04-06 20:02:52,376 : INFO : epoch 883: threshold: 0.884 avg_dist: 0.825 num_edges: 330 2018-04-06 20:02:52,382 : INFO : epoch 884: threshold: 0.885 avg_dist: 0.824 num_edges: 323 2018-04-06 20:02:52,389 : INFO : epoch 885: threshold: 0.886 avg_dist: 0.825 num_edges: 319 2018-04-06 20:02:52,395 : INFO : epoch 886: threshold: 0.887 avg_dist: 0.827 num_edges: 317 2018-04-06 20:02:52,402 : INFO : epoch 887: threshold: 0.888 avg_dist: 0.830 num_edges: 314 2018-04-06 20:02:52,408 : INFO : epoch 888: threshold: 0.889 avg_dist: 0.829 num_edges: 310 2018-04-06 20:02:52,415 : INFO : epoch 889: threshold: 0.890 avg_dist: 0.828 num_edges: 309 2018-04-06 20:02:52,422 : INFO : epoch 890: threshold: 0.891 avg_dist: 0.827 num_edges: 305 2018-04-06 20:02:52,428 : INFO : epoch 891: threshold: 0.892 avg_dist: 0.827 num_edges: 303 2018-04-06 20:02:52,435 : INFO : epoch 892: threshold: 0.893 avg_dist: 0.825 num_edges: 301 2018-04-06 20:02:52,441 : INFO : epoch 893: threshold: 0.894 avg_dist: 0.826 num_edges: 299 2018-04-06 20:02:52,483 : INFO : epoch 894: threshold: 0.895 avg_dist: 0.830 num_edges: 294 2018-04-06 20:02:52,492 : INFO : epoch 895: threshold: 0.896 avg_dist: 0.831 num_edges: 288 2018-04-06 20:02:52,502 : INFO : epoch 896: threshold: 0.897 avg_dist: 0.830 num_edges: 286 2018-04-06 20:02:52,511 : INFO : epoch 897: threshold: 0.898 avg_dist: 0.829 num_edges: 282 2018-04-06 20:02:52,520 : INFO : epoch 898: threshold: 0.899 avg_dist: 0.829 num_edges: 276 2018-04-06 20:02:52,529 : INFO : epoch 899: threshold: 0.900 avg_dist: 0.828 num_edges: 275 2018-04-06 20:02:52,538 : INFO : epoch 900: threshold: 0.901 avg_dist: 0.828 num_edges: 269 2018-04-06 20:02:52,549 : INFO : epoch 901: threshold: 0.902 avg_dist: 0.827 num_edges: 267 2018-04-06 20:02:52,555 : INFO : epoch 902: threshold: 0.903 avg_dist: 0.831 num_edges: 263 2018-04-06 20:02:52,562 : INFO : epoch 903: threshold: 0.904 avg_dist: 0.831 num_edges: 260 2018-04-06 20:02:52,569 : INFO : epoch 904: threshold: 0.905 avg_dist: 0.833 num_edges: 256 2018-04-06 20:02:52,575 : INFO : epoch 905: threshold: 0.906 avg_dist: 0.835 num_edges: 250 2018-04-06 20:02:52,582 : INFO : epoch 906: threshold: 0.907 avg_dist: 0.836 num_edges: 248 2018-04-06 20:02:52,588 : INFO : epoch 907: threshold: 0.908 avg_dist: 0.838 num_edges: 242 2018-04-06 20:02:52,595 : INFO : epoch 908: threshold: 0.909 avg_dist: 0.838 num_edges: 239 2018-04-06 20:02:52,602 : INFO : epoch 909: threshold: 0.910 avg_dist: 0.838 num_edges: 235 2018-04-06 20:02:52,608 : INFO : epoch 910: threshold: 0.911 avg_dist: 0.837 num_edges: 232 2018-04-06 20:02:52,615 : INFO : epoch 911: threshold: 0.912 avg_dist: 0.836 num_edges: 229 2018-04-06 20:02:52,621 : INFO : epoch 912: threshold: 0.913 avg_dist: 0.835 num_edges: 227 2018-04-06 20:02:52,628 : INFO : epoch 913: threshold: 0.914 avg_dist: 0.833 num_edges: 225 2018-04-06 20:02:52,635 : INFO : epoch 914: threshold: 0.915 avg_dist: 0.833 num_edges: 219 2018-04-06 20:02:52,641 : INFO : epoch 915: threshold: 0.916 avg_dist: 0.842 num_edges: 216 2018-04-06 20:02:52,648 : INFO : epoch 916: threshold: 0.917 avg_dist: 0.847 num_edges: 212 2018-04-06 20:02:52,654 : INFO : epoch 917: threshold: 0.918 avg_dist: 0.847 num_edges: 210 2018-04-06 20:02:52,661 : INFO : epoch 918: threshold: 0.919 avg_dist: 0.842 num_edges: 206 2018-04-06 20:02:52,668 : INFO : epoch 919: threshold: 0.920 avg_dist: 0.842 num_edges: 204 2018-04-06 20:02:52,674 : INFO : epoch 920: threshold: 0.921 avg_dist: 0.842 num_edges: 201 2018-04-06 20:02:52,681 : INFO : epoch 921: threshold: 0.922 avg_dist: 0.843 num_edges: 199 2018-04-06 20:02:52,687 : INFO : epoch 922: threshold: 0.923 avg_dist: 0.846 num_edges: 194 2018-04-06 20:02:52,694 : INFO : epoch 923: threshold: 0.924 avg_dist: 0.845 num_edges: 191 2018-04-06 20:02:52,700 : INFO : epoch 924: threshold: 0.925 avg_dist: 0.845 num_edges: 190 2018-04-06 20:02:52,707 : INFO : epoch 925: threshold: 0.926 avg_dist: 0.845 num_edges: 188 2018-04-06 20:02:52,713 : INFO : epoch 926: threshold: 0.927 avg_dist: 0.843 num_edges: 185 2018-04-06 20:02:52,720 : INFO : epoch 927: threshold: 0.928 avg_dist: 0.849 num_edges: 181 2018-04-06 20:02:52,727 : INFO : epoch 928: threshold: 0.929 avg_dist: 0.849 num_edges: 177 2018-04-06 20:02:52,733 : INFO : epoch 929: threshold: 0.930 avg_dist: 0.850 num_edges: 175 2018-04-06 20:02:52,739 : INFO : epoch 930: threshold: 0.931 avg_dist: 0.853 num_edges: 173 2018-04-06 20:02:52,746 : INFO : epoch 931: threshold: 0.932 avg_dist: 0.853 num_edges: 170 2018-04-06 20:02:52,752 : INFO : epoch 932: threshold: 0.933 avg_dist: 0.860 num_edges: 165 2018-04-06 20:02:52,759 : INFO : epoch 933: threshold: 0.934 avg_dist: 0.861 num_edges: 159 2018-04-06 20:02:52,766 : INFO : epoch 934: threshold: 0.935 avg_dist: 0.861 num_edges: 157 2018-04-06 20:02:52,772 : INFO : epoch 935: threshold: 0.936 avg_dist: 0.862 num_edges: 151 2018-04-06 20:02:52,780 : INFO : epoch 936: threshold: 0.937 avg_dist: 0.862 num_edges: 151 2018-04-06 20:02:52,786 : INFO : epoch 937: threshold: 0.938 avg_dist: 0.867 num_edges: 144 2018-04-06 20:02:52,793 : INFO : epoch 938: threshold: 0.939 avg_dist: 0.865 num_edges: 141 2018-04-06 20:02:52,800 : INFO : epoch 939: threshold: 0.940 avg_dist: 0.870 num_edges: 138 2018-04-06 20:02:52,806 : INFO : epoch 940: threshold: 0.941 avg_dist: 0.872 num_edges: 135 2018-04-06 20:02:52,812 : INFO : epoch 941: threshold: 0.942 avg_dist: 0.872 num_edges: 134 2018-04-06 20:02:52,819 : INFO : epoch 942: threshold: 0.943 avg_dist: 0.868 num_edges: 126 2018-04-06 20:02:52,826 : INFO : epoch 943: threshold: 0.944 avg_dist: 0.874 num_edges: 122 2018-04-06 20:02:52,832 : INFO : epoch 944: threshold: 0.945 avg_dist: 0.883 num_edges: 118 2018-04-06 20:02:52,838 : INFO : epoch 945: threshold: 0.946 avg_dist: 0.883 num_edges: 118 2018-04-06 20:02:52,845 : INFO : epoch 946: threshold: 0.947 avg_dist: 0.883 num_edges: 118 2018-04-06 20:02:52,851 : INFO : epoch 947: threshold: 0.948 avg_dist: 0.883 num_edges: 113 2018-04-06 20:02:52,858 : INFO : epoch 948: threshold: 0.949 avg_dist: 0.882 num_edges: 108 2018-04-06 20:02:52,865 : INFO : epoch 949: threshold: 0.950 avg_dist: 0.882 num_edges: 104 2018-04-06 20:02:52,871 : INFO : epoch 950: threshold: 0.951 avg_dist: 0.883 num_edges: 102 2018-04-06 20:02:52,878 : INFO : epoch 951: threshold: 0.952 avg_dist: 0.889 num_edges: 97 2018-04-06 20:02:52,884 : INFO : epoch 952: threshold: 0.953 avg_dist: 0.890 num_edges: 95 2018-04-06 20:02:52,891 : INFO : epoch 953: threshold: 0.954 avg_dist: 0.901 num_edges: 89 2018-04-06 20:02:52,897 : INFO : epoch 954: threshold: 0.955 avg_dist: 0.910 num_edges: 86 2018-04-06 20:02:52,904 : INFO : epoch 955: threshold: 0.956 avg_dist: 0.910 num_edges: 86 2018-04-06 20:02:52,910 : INFO : epoch 956: threshold: 0.957 avg_dist: 0.911 num_edges: 83 2018-04-06 20:02:52,917 : INFO : epoch 957: threshold: 0.958 avg_dist: 0.911 num_edges: 82 2018-04-06 20:02:52,923 : INFO : epoch 958: threshold: 0.959 avg_dist: 0.914 num_edges: 79 2018-04-06 20:02:52,930 : INFO : epoch 959: threshold: 0.960 avg_dist: 0.914 num_edges: 75 2018-04-06 20:02:52,936 : INFO : epoch 960: threshold: 0.961 avg_dist: 0.919 num_edges: 72 2018-04-06 20:02:52,943 : INFO : epoch 961: threshold: 0.962 avg_dist: 0.920 num_edges: 69 2018-04-06 20:02:52,949 : INFO : epoch 962: threshold: 0.963 avg_dist: 0.921 num_edges: 66 2018-04-06 20:02:52,956 : INFO : epoch 963: threshold: 0.964 avg_dist: 0.921 num_edges: 66 2018-04-06 20:02:52,962 : INFO : epoch 964: threshold: 0.965 avg_dist: 0.923 num_edges: 64 2018-04-06 20:02:52,969 : INFO : epoch 965: threshold: 0.966 avg_dist: 0.923 num_edges: 59 2018-04-06 20:02:52,975 : INFO : epoch 966: threshold: 0.967 avg_dist: 0.924 num_edges: 57 2018-04-06 20:02:52,982 : INFO : epoch 967: threshold: 0.968 avg_dist: 0.926 num_edges: 55 2018-04-06 20:02:52,988 : INFO : epoch 968: threshold: 0.969 avg_dist: 0.926 num_edges: 53 2018-04-06 20:02:52,995 : INFO : epoch 969: threshold: 0.970 avg_dist: 0.927 num_edges: 52 2018-04-06 20:02:53,001 : INFO : epoch 970: threshold: 0.971 avg_dist: 0.929 num_edges: 50 2018-04-06 20:02:53,008 : INFO : epoch 971: threshold: 0.972 avg_dist: 0.936 num_edges: 48 2018-04-06 20:02:53,014 : INFO : epoch 972: threshold: 0.973 avg_dist: 0.936 num_edges: 47 2018-04-06 20:02:53,021 : INFO : epoch 973: threshold: 0.974 avg_dist: 0.939 num_edges: 45 2018-04-06 20:02:53,027 : INFO : epoch 974: threshold: 0.975 avg_dist: 0.941 num_edges: 42 2018-04-06 20:02:53,034 : INFO : epoch 975: threshold: 0.976 avg_dist: 0.942 num_edges: 41 2018-04-06 20:02:53,040 : INFO : epoch 976: threshold: 0.977 avg_dist: 0.942 num_edges: 41 2018-04-06 20:02:53,047 : INFO : epoch 977: threshold: 0.978 avg_dist: 0.948 num_edges: 38 2018-04-06 20:02:53,053 : INFO : epoch 978: threshold: 0.979 avg_dist: 0.948 num_edges: 35 2018-04-06 20:02:53,060 : INFO : epoch 979: threshold: 0.980 avg_dist: 0.951 num_edges: 32 2018-04-06 20:02:53,066 : INFO : epoch 980: threshold: 0.981 avg_dist: 0.953 num_edges: 31 2018-04-06 20:02:53,073 : INFO : epoch 981: threshold: 0.982 avg_dist: 0.956 num_edges: 30 2018-04-06 20:02:53,079 : INFO : epoch 982: threshold: 0.983 avg_dist: 0.959 num_edges: 28 2018-04-06 20:02:53,085 : INFO : epoch 983: threshold: 0.984 avg_dist: 0.960 num_edges: 26 2018-04-06 20:02:53,093 : INFO : epoch 984: threshold: 0.985 avg_dist: 0.961 num_edges: 25 2018-04-06 20:02:53,099 : INFO : epoch 985: threshold: 0.986 avg_dist: 0.961 num_edges: 24 2018-04-06 20:02:53,106 : INFO : epoch 986: threshold: 0.987 avg_dist: 0.961 num_edges: 23 2018-04-06 20:02:53,112 : INFO : epoch 987: threshold: 0.988 avg_dist: 0.967 num_edges: 21 2018-04-06 20:02:53,119 : INFO : epoch 988: threshold: 0.989 avg_dist: 0.974 num_edges: 16 2018-04-06 20:02:53,125 : INFO : epoch 989: threshold: 0.990 avg_dist: 0.977 num_edges: 13 2018-04-06 20:02:53,132 : INFO : epoch 990: threshold: 0.991 avg_dist: 0.980 num_edges: 10 2018-04-06 20:02:53,138 : INFO : epoch 991: threshold: 0.992 avg_dist: 0.987 num_edges: 7 2018-04-06 20:02:53,144 : INFO : epoch 992: threshold: 0.993 avg_dist: 0.987 num_edges: 5 2018-04-06 20:02:53,151 : INFO : epoch 993: threshold: 0.994 avg_dist: 0.990 num_edges: 4 2018-04-06 20:02:53,157 : INFO : epoch 994: threshold: 0.995 avg_dist: 0.994 num_edges: 2 2018-04-06 20:02:53,164 : INFO : epoch 995: threshold: 0.996 avg_dist: 0.994 num_edges: 2 2018-04-06 20:02:53,171 : INFO : epoch 996: threshold: 0.997 avg_dist: 0.995 num_edges: 1 2018-04-06 20:02:53,178 : INFO : epoch 997: threshold: 0.998 avg_dist: 0.995 num_edges: 1 2018-04-06 20:02:53,184 : INFO : epoch 998: threshold: 0.999 avg_dist: 0.995 num_edges: 1
threshold: 0.837 dist 0.783736 (735, 0.736) edges 473/471.0 (836)
threshold: 0.837 dist 0.783736 (735, 0.736) edges 473/471.0 (836)
In [33]:
# Set the edges:
topicnet_adj_mat = set_edges(cosim, 0.837, binary=True)
2018-04-11 22:38:05,845 : INFO : dimension: 195, 195
In [12]:
# Save the topicnet adj-mat
topicnet_adj_mat.dump(open('./res/topicnet20yr_adjmat.pkl','wb'))
Visualise¶
Thresholding:¶
In [21]:
f = threshold_plot(
thresholds,
distances,
edges,
best_threshold,
closest_edges,
best_j_dist_threshold,
lowest_avg_distances,
ground_truth_num_edges=np.sum(adj_mat)//2,
cosim=cosim
)
In [14]:
f.savefig(
'./IMG/topicnet20yr_thresholding.png', format='png', bbox_inches='tight')
As series of matrices:¶
In [34]:
f = plt.figure(figsize=(15, 5))
# ORIGINAL COLALB NET
ax1 = f.add_subplot(131)
ax1.axis('off')
sns.heatmap(adj_mat, cbar=False, square=True)
ax1.set_title('infnet20yr\n(#Edges={})'.format(int(np.sum(adj_mat) // 2)))
# COSIM
ax2 = f.add_subplot(132)
ax2.axis('off')
sns.heatmap(
cosim,
cbar=False,
square=True,
ax=ax2,
vmax=1,
vmin=0,
robust=True,
cmap='gnuplot2')
ax2.set_title('Cosine similairty\nbetween researchers')
# FINAL :
ax3 = f.add_subplot(133)
ax3.axis('off')
sns.heatmap(topicnet_adj_mat, cbar=False, square=True, ax=ax3)
ax3.set_title('topicnet20yr\n($\epsilon$={:.3f}, #Edges={})'.format(
best_threshold,
int(np.sum(topicnet_adj_mat) // 2)))
f.tight_layout()
In [16]:
f.savefig('./IMG/topicnet20yr_adjmats.png')
As networks:¶
In [35]:
g_topicnet20yr = adj_mat_to_graph(topicnet_adj_mat, collabnet.id.tolist())
In [36]:
print(nx.info(g_topicnet20yr))
Name: Type: Graph Number of nodes: 169 Number of edges: 473 Average degree: 5.5976
In [37]:
artist.draw_default_layout(
g_topicnet20yr,
lookup_poinf=get_lookup_poinf(),
file_prefix='topicnet20yr',
SAVE_GRAPHS=True)
2018-04-11 22:38:23,112 : INFO : SAVE_GRAPHS: True
In [38]:
artist.draw_circular_layout(
g_topicnet20yr,
get_lookup_poinf(),
file_prefix='topicnet20yr',
SAVE_GRAPHS=True)
2018-04-11 22:38:26,170 : INFO : SAVE_GRAPHS: True
In [39]:
nx.draw(
g_topicnet20yr,
pos=nx.spring_layout(g_topicnet20yr),
node_size=30,
node_color=artist.color_by_inst(g_topicnet20yr, get_lookup_poinf()))
In [40]:
pos_topicnet20yr = nx.spring_layout(g_topicnet20yr,k=.1)
In [41]:
# Visualise with topic label
partition_dict = {}
topicnet['list_tm'] = topicnet.topic_probs.apply(lambda x: to_list(x, 30))
n = 0
for i, nodes in enumerate(nx.connected_components(g_topicnet20yr)):
n += len(nodes)
a = topicnet[topicnet.id.isin(nodes)].list_tm.tolist()
topic_dist = np.mean(a, axis=0)
partition_dict[i] = {
'nodes': nodes,
'topic_dist': topic_dist,
'most_probable_topic': np.argmax(topic_dist)
}
topics = [
3, 7, 14, 4, 24, 0, 2, 17, 20, 16, 11, 21, 13, 8, 18, 1, 5, 22, 19, 23,
9,12, 15, 6, 10
]
labels = [
'Machine Translation', 'IRR', 'Neuroscience', 'IRR', 'Bioinformatics',
'Computer Vision', 'Speech Synethesis', 'Relational Algebra (?)',
'Formal Language', 'Machine Learning', 'Formal Verification',
'Robot Control', 'Database System', 'NLP', 'Agent Based System',
'Compiler Optimisation', '??', 'Communication Network', '??',
'Computer simulation', 'Cognitive Science', 'Algorithm',
'Information Rerieval', 'Formal Logic (?)', 'Quantum Computing (?)'
]
TOPIC_DICT = dict((a,b) for a,b in zip(topics,labels))
f = plt.figure(figsize=(20, 12))
ax = f.add_subplot(111)
nx.draw(
g_topicnet20yr,
pos=pos_topicnet20yr,
ax=ax,
node_size=50,
alpha=.9,
node_color=artist.color_by_inst(g_topicnet20yr, get_lookup_poinf()))
for _, node_dict in list(partition_dict.items()):
# calculate average position:
pos_x, pos_y = 0, 0
for node in node_dict['nodes']:
pos_x += pos_topicnet20yr[node][0]
pos_y += pos_topicnet20yr[node][1]
pos_x /= len(node_dict['nodes'])
pos_y /= len(node_dict['nodes'])
topic_num = node_dict['most_probable_topic']
ax.text(
pos_x-.1,
pos_y - .1,
"{}-{}".format(topic_num + 1, TOPIC_DICT[topic_num]),
withdash=True,
size=17,
weight='bold')
# print((node_dict['most_probable_topic']))
artist.add_inst_labels(ax)
ax.legend(
loc='upper center',
bbox_to_anchor=(0.9, 1.),
ncol=1,
title='institutes',
fontsize=12,
fancybox=True,
shadow=True)
f.tight_layout()
In [34]:
f.savefig('IMG/topicnet20yr_w_topics.pdf',format='pdf',bbox_inches='tight')
Descriptive statistics¶
In [42]:
gccs = generateGCC(g_topicnet20yr)
component 1: 15.38% component 2: 14.20% component 3: 11.83% component 4: 11.24% component 5: 7.69% component 6: 5.33% component 7: 4.73% component 8: 4.73% component 9: 4.14% component 10: 4.14% component 11: 3.55% component 12: 2.96% component 13: 2.37% component 14: 2.37% component 15: 1.78% component 16: 1.18% component 17: 1.18% component 18: 1.18%
In [43]:
nx.average_clustering(g_topicnet20yr,count_zeros=False)
Out[43]:
0.8174458366359769
In [44]:
# Clustering coefficient
c_coeff,avg=clustering_coeff(g_topicnet20yr)
print(avg)
0.6868479810787503
In [45]:
# transitivity
nx.transitivity(g_topicnet20yr)
Out[45]:
0.7556427870461236
In [46]:
# degree dist
nx.degree_histogram(g_topicnet20yr)
Out[46]:
[0, 19, 18, 21, 12, 10, 16, 26, 14, 13, 9, 6, 3, 0, 0, 1, 1]
Deriving topicnetref-20yr¶
In [47]:
# Since we are using the same base, we do not need to reload it again!
# Loading the new topic model:
tm = models.LdaModel.load('../../topicModel/src/tmdblp/tm_dblp')
dict_tm = Dictionary.load(
'../../data/data_dblp/corpora/dictionary.dblp.1997-2017')
2018-04-11 22:38:45,336 : INFO : loading LdaModel object from ../../topicModel/src/tmdblp/tm_dblp 2018-04-11 22:38:45,343 : INFO : loading expElogbeta from ../../topicModel/src/tmdblp/tm_dblp.expElogbeta.npy with mmap=None 2018-04-11 22:38:45,936 : INFO : setting ignored attribute id2word to None 2018-04-11 22:38:45,937 : INFO : setting ignored attribute dispatcher to None 2018-04-11 22:38:45,938 : INFO : setting ignored attribute state to None 2018-04-11 22:38:45,938 : INFO : loaded ../../topicModel/src/tmdblp/tm_dblp 2018-04-11 22:38:45,939 : INFO : loading LdaModel object from ../../topicModel/src/tmdblp/tm_dblp.state 2018-04-11 22:38:47,188 : INFO : loaded ../../topicModel/src/tmdblp/tm_dblp.state 2018-04-11 22:38:47,232 : INFO : loading Dictionary object from ../../data/data_dblp/corpora/dictionary.dblp.1997-2017 2018-04-11 22:38:47,280 : INFO : loaded ../../data/data_dblp/corpora/dictionary.dblp.1997-2017
In [48]:
# Generate topic probabilities for each publication using the dictionary for DBLP
tm.minimum_probability = 0.001
topicnet['topic_probs'] = topicnet.toks.apply(
lambda a: tm.get_document_topics(dict_tm.doc2bow(a)) if len(a) else None)
In [49]:
# Generate cosine similarity
cosim = compare_researchers(topicnet.topic_probs.tolist(), tm.num_topics)
In [50]:
# find best threshold
thresholds, edges, distances, closest_edges, best_threshold, lowest_avg_distances, best_j_dist_epoch, best_j_dist_threshold = \
find_best_threshold(adj_mat, cosim, num_iter=10000, step_size=0.001)
2018-04-11 22:39:00,146 : INFO : Number of ground_truth_edges: 471.0 2018-04-11 22:39:00,148 : INFO : binary edges: True 2018-04-11 22:39:00,160 : INFO : epoch 0: threshold: 0.001 avg_dist: 0.975 num_edges: 18900 2018-04-11 22:39:00,174 : INFO : epoch 1: threshold: 0.002 avg_dist: 0.975 num_edges: 18895 2018-04-11 22:39:00,184 : INFO : epoch 2: threshold: 0.003 avg_dist: 0.975 num_edges: 18885 2018-04-11 22:39:00,194 : INFO : epoch 3: threshold: 0.004 avg_dist: 0.975 num_edges: 18864 2018-04-11 22:39:00,205 : INFO : epoch 4: threshold: 0.005 avg_dist: 0.975 num_edges: 18843 2018-04-11 22:39:00,218 : INFO : epoch 5: threshold: 0.006 avg_dist: 0.975 num_edges: 18817 2018-04-11 22:39:00,229 : INFO : epoch 6: threshold: 0.007 avg_dist: 0.975 num_edges: 18783 2018-04-11 22:39:00,237 : INFO : epoch 7: threshold: 0.008 avg_dist: 0.975 num_edges: 18745 2018-04-11 22:39:00,247 : INFO : epoch 8: threshold: 0.009 avg_dist: 0.975 num_edges: 18713 2018-04-11 22:39:00,256 : INFO : epoch 9: threshold: 0.010 avg_dist: 0.975 num_edges: 18679 2018-04-11 22:39:00,265 : INFO : epoch 10: threshold: 0.011 avg_dist: 0.975 num_edges: 18645 2018-04-11 22:39:00,274 : INFO : epoch 11: threshold: 0.012 avg_dist: 0.975 num_edges: 18607 2018-04-11 22:39:00,282 : INFO : epoch 12: threshold: 0.013 avg_dist: 0.975 num_edges: 18560 2018-04-11 22:39:00,290 : INFO : epoch 13: threshold: 0.014 avg_dist: 0.975 num_edges: 18520 2018-04-11 22:39:00,300 : INFO : epoch 14: threshold: 0.015 avg_dist: 0.975 num_edges: 18471 2018-04-11 22:39:00,309 : INFO : epoch 15: threshold: 0.016 avg_dist: 0.975 num_edges: 18415 2018-04-11 22:39:00,317 : INFO : epoch 16: threshold: 0.017 avg_dist: 0.975 num_edges: 18357 2018-04-11 22:39:00,331 : INFO : epoch 17: threshold: 0.018 avg_dist: 0.974 num_edges: 18308 2018-04-11 22:39:00,340 : INFO : epoch 18: threshold: 0.019 avg_dist: 0.974 num_edges: 18248 2018-04-11 22:39:00,351 : INFO : epoch 19: threshold: 0.020 avg_dist: 0.974 num_edges: 18182 2018-04-11 22:39:00,361 : INFO : epoch 20: threshold: 0.021 avg_dist: 0.974 num_edges: 18117 2018-04-11 22:39:00,371 : INFO : epoch 21: threshold: 0.022 avg_dist: 0.974 num_edges: 18053 2018-04-11 22:39:00,379 : INFO : epoch 22: threshold: 0.023 avg_dist: 0.974 num_edges: 17979 2018-04-11 22:39:00,392 : INFO : epoch 23: threshold: 0.024 avg_dist: 0.974 num_edges: 17900 2018-04-11 22:39:00,406 : INFO : epoch 24: threshold: 0.025 avg_dist: 0.974 num_edges: 17829 2018-04-11 22:39:00,414 : INFO : epoch 25: threshold: 0.026 avg_dist: 0.974 num_edges: 17763 2018-04-11 22:39:00,422 : INFO : epoch 26: threshold: 0.027 avg_dist: 0.974 num_edges: 17692 2018-04-11 22:39:00,432 : INFO : epoch 27: threshold: 0.028 avg_dist: 0.974 num_edges: 17613 2018-04-11 22:39:00,444 : INFO : epoch 28: threshold: 0.029 avg_dist: 0.973 num_edges: 17538 2018-04-11 22:39:00,452 : INFO : epoch 29: threshold: 0.030 avg_dist: 0.973 num_edges: 17444 2018-04-11 22:39:00,464 : INFO : epoch 30: threshold: 0.031 avg_dist: 0.973 num_edges: 17345 2018-04-11 22:39:00,476 : INFO : epoch 31: threshold: 0.032 avg_dist: 0.973 num_edges: 17253 2018-04-11 22:39:00,486 : INFO : epoch 32: threshold: 0.033 avg_dist: 0.973 num_edges: 17150 2018-04-11 22:39:00,495 : INFO : epoch 33: threshold: 0.034 avg_dist: 0.973 num_edges: 17067 2018-04-11 22:39:00,505 : INFO : epoch 34: threshold: 0.035 avg_dist: 0.973 num_edges: 16987 2018-04-11 22:39:00,514 : INFO : epoch 35: threshold: 0.036 avg_dist: 0.972 num_edges: 16913 2018-04-11 22:39:00,522 : INFO : epoch 36: threshold: 0.037 avg_dist: 0.972 num_edges: 16828 2018-04-11 22:39:00,539 : INFO : epoch 37: threshold: 0.038 avg_dist: 0.972 num_edges: 16747 2018-04-11 22:39:00,551 : INFO : epoch 38: threshold: 0.039 avg_dist: 0.972 num_edges: 16662 2018-04-11 22:39:00,565 : INFO : epoch 39: threshold: 0.040 avg_dist: 0.972 num_edges: 16586 2018-04-11 22:39:00,580 : INFO : epoch 40: threshold: 0.041 avg_dist: 0.972 num_edges: 16502 2018-04-11 22:39:00,591 : INFO : epoch 41: threshold: 0.042 avg_dist: 0.971 num_edges: 16424 2018-04-11 22:39:00,604 : INFO : epoch 42: threshold: 0.043 avg_dist: 0.971 num_edges: 16339 2018-04-11 22:39:00,614 : INFO : epoch 43: threshold: 0.044 avg_dist: 0.971 num_edges: 16252 2018-04-11 22:39:00,625 : INFO : epoch 44: threshold: 0.045 avg_dist: 0.971 num_edges: 16178 2018-04-11 22:39:00,634 : INFO : epoch 45: threshold: 0.046 avg_dist: 0.971 num_edges: 16099 2018-04-11 22:39:00,643 : INFO : epoch 46: threshold: 0.047 avg_dist: 0.971 num_edges: 16014 2018-04-11 22:39:00,652 : INFO : epoch 47: threshold: 0.048 avg_dist: 0.970 num_edges: 15916 2018-04-11 22:39:00,662 : INFO : epoch 48: threshold: 0.049 avg_dist: 0.970 num_edges: 15833 2018-04-11 22:39:00,674 : INFO : epoch 49: threshold: 0.050 avg_dist: 0.970 num_edges: 15738 2018-04-11 22:39:00,685 : INFO : epoch 50: threshold: 0.051 avg_dist: 0.970 num_edges: 15658 2018-04-11 22:39:00,693 : INFO : epoch 51: threshold: 0.052 avg_dist: 0.970 num_edges: 15582 2018-04-11 22:39:00,701 : INFO : epoch 52: threshold: 0.053 avg_dist: 0.970 num_edges: 15476 2018-04-11 22:39:00,712 : INFO : epoch 53: threshold: 0.054 avg_dist: 0.969 num_edges: 15391 2018-04-11 22:39:00,727 : INFO : epoch 54: threshold: 0.055 avg_dist: 0.969 num_edges: 15287 2018-04-11 22:39:00,740 : INFO : epoch 55: threshold: 0.056 avg_dist: 0.969 num_edges: 15204 2018-04-11 22:39:00,752 : INFO : epoch 56: threshold: 0.057 avg_dist: 0.969 num_edges: 15123 2018-04-11 22:39:00,761 : INFO : epoch 57: threshold: 0.058 avg_dist: 0.969 num_edges: 15061 2018-04-11 22:39:00,768 : INFO : epoch 58: threshold: 0.059 avg_dist: 0.968 num_edges: 14963 2018-04-11 22:39:00,780 : INFO : epoch 59: threshold: 0.060 avg_dist: 0.968 num_edges: 14891 2018-04-11 22:39:00,793 : INFO : epoch 60: threshold: 0.061 avg_dist: 0.968 num_edges: 14809 2018-04-11 22:39:00,805 : INFO : epoch 61: threshold: 0.062 avg_dist: 0.968 num_edges: 14724 2018-04-11 22:39:00,814 : INFO : epoch 62: threshold: 0.063 avg_dist: 0.968 num_edges: 14638 2018-04-11 22:39:00,828 : INFO : epoch 63: threshold: 0.064 avg_dist: 0.967 num_edges: 14568 2018-04-11 22:39:00,836 : INFO : epoch 64: threshold: 0.065 avg_dist: 0.967 num_edges: 14485 2018-04-11 22:39:00,847 : INFO : epoch 65: threshold: 0.066 avg_dist: 0.967 num_edges: 14398 2018-04-11 22:39:00,856 : INFO : epoch 66: threshold: 0.067 avg_dist: 0.967 num_edges: 14324 2018-04-11 22:39:00,866 : INFO : epoch 67: threshold: 0.068 avg_dist: 0.967 num_edges: 14235 2018-04-11 22:39:00,877 : INFO : epoch 68: threshold: 0.069 avg_dist: 0.966 num_edges: 14155 2018-04-11 22:39:00,888 : INFO : epoch 69: threshold: 0.070 avg_dist: 0.966 num_edges: 14076 2018-04-11 22:39:00,902 : INFO : epoch 70: threshold: 0.071 avg_dist: 0.966 num_edges: 13999 2018-04-11 22:39:00,912 : INFO : epoch 71: threshold: 0.072 avg_dist: 0.966 num_edges: 13906 2018-04-11 22:39:00,922 : INFO : epoch 72: threshold: 0.073 avg_dist: 0.965 num_edges: 13825 2018-04-11 22:39:00,933 : INFO : epoch 73: threshold: 0.074 avg_dist: 0.965 num_edges: 13733 2018-04-11 22:39:00,946 : INFO : epoch 74: threshold: 0.075 avg_dist: 0.965 num_edges: 13662 2018-04-11 22:39:00,955 : INFO : epoch 75: threshold: 0.076 avg_dist: 0.965 num_edges: 13578 2018-04-11 22:39:00,965 : INFO : epoch 76: threshold: 0.077 avg_dist: 0.965 num_edges: 13508 2018-04-11 22:39:00,979 : INFO : epoch 77: threshold: 0.078 avg_dist: 0.964 num_edges: 13432 2018-04-11 22:39:00,988 : INFO : epoch 78: threshold: 0.079 avg_dist: 0.964 num_edges: 13361 2018-04-11 22:39:01,002 : INFO : epoch 79: threshold: 0.080 avg_dist: 0.964 num_edges: 13289 2018-04-11 22:39:01,012 : INFO : epoch 80: threshold: 0.081 avg_dist: 0.964 num_edges: 13217 2018-04-11 22:39:01,023 : INFO : epoch 81: threshold: 0.082 avg_dist: 0.963 num_edges: 13131 2018-04-11 22:39:01,035 : INFO : epoch 82: threshold: 0.083 avg_dist: 0.963 num_edges: 13072 2018-04-11 22:39:01,043 : INFO : epoch 83: threshold: 0.084 avg_dist: 0.963 num_edges: 12986 2018-04-11 22:39:01,055 : INFO : epoch 84: threshold: 0.085 avg_dist: 0.963 num_edges: 12919 2018-04-11 22:39:01,067 : INFO : epoch 85: threshold: 0.086 avg_dist: 0.962 num_edges: 12830 2018-04-11 22:39:01,075 : INFO : epoch 86: threshold: 0.087 avg_dist: 0.962 num_edges: 12758 2018-04-11 22:39:01,086 : INFO : epoch 87: threshold: 0.088 avg_dist: 0.962 num_edges: 12689 2018-04-11 22:39:01,095 : INFO : epoch 88: threshold: 0.089 avg_dist: 0.961 num_edges: 12615 2018-04-11 22:39:01,111 : INFO : epoch 89: threshold: 0.090 avg_dist: 0.961 num_edges: 12540 2018-04-11 22:39:01,120 : INFO : epoch 90: threshold: 0.091 avg_dist: 0.961 num_edges: 12461 2018-04-11 22:39:01,131 : INFO : epoch 91: threshold: 0.092 avg_dist: 0.961 num_edges: 12381 2018-04-11 22:39:01,142 : INFO : epoch 92: threshold: 0.093 avg_dist: 0.960 num_edges: 12299 2018-04-11 22:39:01,151 : INFO : epoch 93: threshold: 0.094 avg_dist: 0.960 num_edges: 12235 2018-04-11 22:39:01,165 : INFO : epoch 94: threshold: 0.095 avg_dist: 0.960 num_edges: 12162 2018-04-11 22:39:01,175 : INFO : epoch 95: threshold: 0.096 avg_dist: 0.960 num_edges: 12079 2018-04-11 22:39:01,190 : INFO : epoch 96: threshold: 0.097 avg_dist: 0.959 num_edges: 12010 2018-04-11 22:39:01,200 : INFO : epoch 97: threshold: 0.098 avg_dist: 0.959 num_edges: 11941 2018-04-11 22:39:01,209 : INFO : epoch 98: threshold: 0.099 avg_dist: 0.959 num_edges: 11885 2018-04-11 22:39:01,221 : INFO : epoch 99: threshold: 0.100 avg_dist: 0.959 num_edges: 11821 2018-04-11 22:39:01,231 : INFO : epoch 100: threshold: 0.101 avg_dist: 0.958 num_edges: 11751 2018-04-11 22:39:01,238 : INFO : epoch 101: threshold: 0.102 avg_dist: 0.958 num_edges: 11681 2018-04-11 22:39:01,253 : INFO : epoch 102: threshold: 0.103 avg_dist: 0.958 num_edges: 11612 2018-04-11 22:39:01,264 : INFO : epoch 103: threshold: 0.104 avg_dist: 0.957 num_edges: 11544 2018-04-11 22:39:01,273 : INFO : epoch 104: threshold: 0.105 avg_dist: 0.957 num_edges: 11476 2018-04-11 22:39:01,282 : INFO : epoch 105: threshold: 0.106 avg_dist: 0.957 num_edges: 11404 2018-04-11 22:39:01,293 : INFO : epoch 106: threshold: 0.107 avg_dist: 0.956 num_edges: 11329 2018-04-11 22:39:01,302 : INFO : epoch 107: threshold: 0.108 avg_dist: 0.956 num_edges: 11249 2018-04-11 22:39:01,313 : INFO : epoch 108: threshold: 0.109 avg_dist: 0.956 num_edges: 11197 2018-04-11 22:39:01,322 : INFO : epoch 109: threshold: 0.110 avg_dist: 0.956 num_edges: 11122 2018-04-11 22:39:01,336 : INFO : epoch 110: threshold: 0.111 avg_dist: 0.955 num_edges: 11055 2018-04-11 22:39:01,347 : INFO : epoch 111: threshold: 0.112 avg_dist: 0.955 num_edges: 10987 2018-04-11 22:39:01,359 : INFO : epoch 112: threshold: 0.113 avg_dist: 0.955 num_edges: 10934 2018-04-11 22:39:01,370 : INFO : epoch 113: threshold: 0.114 avg_dist: 0.955 num_edges: 10876 2018-04-11 22:39:01,380 : INFO : epoch 114: threshold: 0.115 avg_dist: 0.954 num_edges: 10823 2018-04-11 22:39:01,390 : INFO : epoch 115: threshold: 0.116 avg_dist: 0.954 num_edges: 10760 2018-04-11 22:39:01,401 : INFO : epoch 116: threshold: 0.117 avg_dist: 0.954 num_edges: 10705 2018-04-11 22:39:01,408 : INFO : epoch 117: threshold: 0.118 avg_dist: 0.954 num_edges: 10654 2018-04-11 22:39:01,420 : INFO : epoch 118: threshold: 0.119 avg_dist: 0.953 num_edges: 10600 2018-04-11 22:39:01,434 : INFO : epoch 119: threshold: 0.120 avg_dist: 0.953 num_edges: 10550 2018-04-11 22:39:01,444 : INFO : epoch 120: threshold: 0.121 avg_dist: 0.953 num_edges: 10484 2018-04-11 22:39:01,452 : INFO : epoch 121: threshold: 0.122 avg_dist: 0.953 num_edges: 10429 2018-04-11 22:39:01,460 : INFO : epoch 122: threshold: 0.123 avg_dist: 0.952 num_edges: 10360 2018-04-11 22:39:01,470 : INFO : epoch 123: threshold: 0.124 avg_dist: 0.952 num_edges: 10299 2018-04-11 22:39:01,480 : INFO : epoch 124: threshold: 0.125 avg_dist: 0.952 num_edges: 10243 2018-04-11 22:39:01,491 : INFO : epoch 125: threshold: 0.126 avg_dist: 0.951 num_edges: 10190 2018-04-11 22:39:01,499 : INFO : epoch 126: threshold: 0.127 avg_dist: 0.951 num_edges: 10143 2018-04-11 22:39:01,507 : INFO : epoch 127: threshold: 0.128 avg_dist: 0.951 num_edges: 10071 2018-04-11 22:39:01,517 : INFO : epoch 128: threshold: 0.129 avg_dist: 0.951 num_edges: 10019 2018-04-11 22:39:01,526 : INFO : epoch 129: threshold: 0.130 avg_dist: 0.950 num_edges: 9967 2018-04-11 22:39:01,535 : INFO : epoch 130: threshold: 0.131 avg_dist: 0.950 num_edges: 9919 2018-04-11 22:39:01,543 : INFO : epoch 131: threshold: 0.132 avg_dist: 0.950 num_edges: 9873 2018-04-11 22:39:01,550 : INFO : epoch 132: threshold: 0.133 avg_dist: 0.949 num_edges: 9822 2018-04-11 22:39:01,558 : INFO : epoch 133: threshold: 0.134 avg_dist: 0.949 num_edges: 9768 2018-04-11 22:39:01,566 : INFO : epoch 134: threshold: 0.135 avg_dist: 0.949 num_edges: 9735 2018-04-11 22:39:01,575 : INFO : epoch 135: threshold: 0.136 avg_dist: 0.949 num_edges: 9680 2018-04-11 22:39:01,586 : INFO : epoch 136: threshold: 0.137 avg_dist: 0.948 num_edges: 9624 2018-04-11 22:39:01,593 : INFO : epoch 137: threshold: 0.138 avg_dist: 0.948 num_edges: 9577 2018-04-11 22:39:01,601 : INFO : epoch 138: threshold: 0.139 avg_dist: 0.948 num_edges: 9520 2018-04-11 22:39:01,610 : INFO : epoch 139: threshold: 0.140 avg_dist: 0.947 num_edges: 9477 2018-04-11 22:39:01,619 : INFO : epoch 140: threshold: 0.141 avg_dist: 0.947 num_edges: 9417 2018-04-11 22:39:01,626 : INFO : epoch 141: threshold: 0.142 avg_dist: 0.947 num_edges: 9375 2018-04-11 22:39:01,634 : INFO : epoch 142: threshold: 0.143 avg_dist: 0.947 num_edges: 9324 2018-04-11 22:39:01,641 : INFO : epoch 143: threshold: 0.144 avg_dist: 0.946 num_edges: 9267 2018-04-11 22:39:01,648 : INFO : epoch 144: threshold: 0.145 avg_dist: 0.946 num_edges: 9213 2018-04-11 22:39:01,657 : INFO : epoch 145: threshold: 0.146 avg_dist: 0.946 num_edges: 9168 2018-04-11 22:39:01,664 : INFO : epoch 146: threshold: 0.147 avg_dist: 0.945 num_edges: 9117 2018-04-11 22:39:01,672 : INFO : epoch 147: threshold: 0.148 avg_dist: 0.945 num_edges: 9053 2018-04-11 22:39:01,680 : INFO : epoch 148: threshold: 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epoch 159: threshold: 0.160 avg_dist: 0.942 num_edges: 8524 2018-04-11 22:39:01,783 : INFO : epoch 160: threshold: 0.161 avg_dist: 0.941 num_edges: 8484 2018-04-11 22:39:01,791 : INFO : epoch 161: threshold: 0.162 avg_dist: 0.941 num_edges: 8431 2018-04-11 22:39:01,800 : INFO : epoch 162: threshold: 0.163 avg_dist: 0.941 num_edges: 8392 2018-04-11 22:39:01,808 : INFO : epoch 163: threshold: 0.164 avg_dist: 0.940 num_edges: 8343 2018-04-11 22:39:01,816 : INFO : epoch 164: threshold: 0.165 avg_dist: 0.940 num_edges: 8300 2018-04-11 22:39:01,824 : INFO : epoch 165: threshold: 0.166 avg_dist: 0.940 num_edges: 8260 2018-04-11 22:39:01,833 : INFO : epoch 166: threshold: 0.167 avg_dist: 0.939 num_edges: 8216 2018-04-11 22:39:01,841 : INFO : epoch 167: threshold: 0.168 avg_dist: 0.939 num_edges: 8180 2018-04-11 22:39:01,854 : INFO : epoch 168: threshold: 0.169 avg_dist: 0.939 num_edges: 8140 2018-04-11 22:39:01,861 : INFO : epoch 169: threshold: 0.170 avg_dist: 0.938 num_edges: 8096 2018-04-11 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epoch 718: threshold: 0.719 avg_dist: 0.795 num_edges: 1132 2018-04-11 22:39:06,621 : INFO : epoch 719: threshold: 0.720 avg_dist: 0.794 num_edges: 1131 2018-04-11 22:39:06,630 : INFO : epoch 720: threshold: 0.721 avg_dist: 0.794 num_edges: 1126 2018-04-11 22:39:06,637 : INFO : epoch 721: threshold: 0.722 avg_dist: 0.794 num_edges: 1125 2018-04-11 22:39:06,644 : INFO : epoch 722: threshold: 0.723 avg_dist: 0.794 num_edges: 1116 2018-04-11 22:39:06,654 : INFO : epoch 723: threshold: 0.724 avg_dist: 0.793 num_edges: 1107 2018-04-11 22:39:06,667 : INFO : epoch 724: threshold: 0.725 avg_dist: 0.798 num_edges: 1103 2018-04-11 22:39:06,675 : INFO : epoch 725: threshold: 0.726 avg_dist: 0.800 num_edges: 1096 2018-04-11 22:39:06,684 : INFO : epoch 726: threshold: 0.727 avg_dist: 0.800 num_edges: 1094 2018-04-11 22:39:06,691 : INFO : epoch 727: threshold: 0.728 avg_dist: 0.799 num_edges: 1088 2018-04-11 22:39:06,699 : INFO : epoch 728: threshold: 0.729 avg_dist: 0.803 num_edges: 1082 2018-04-11 22:39:06,713 : INFO : epoch 729: threshold: 0.730 avg_dist: 0.802 num_edges: 1077 2018-04-11 22:39:06,726 : INFO : epoch 730: threshold: 0.731 avg_dist: 0.801 num_edges: 1069 2018-04-11 22:39:06,734 : INFO : epoch 731: threshold: 0.732 avg_dist: 0.804 num_edges: 1064 2018-04-11 22:39:06,742 : INFO : epoch 732: threshold: 0.733 avg_dist: 0.804 num_edges: 1059 2018-04-11 22:39:06,749 : INFO : epoch 733: threshold: 0.734 avg_dist: 0.804 num_edges: 1053 2018-04-11 22:39:06,757 : INFO : epoch 734: threshold: 0.735 avg_dist: 0.804 num_edges: 1048 2018-04-11 22:39:06,764 : INFO : epoch 735: threshold: 0.736 avg_dist: 0.805 num_edges: 1041 2018-04-11 22:39:06,773 : INFO : epoch 736: threshold: 0.737 avg_dist: 0.805 num_edges: 1035 2018-04-11 22:39:06,781 : INFO : epoch 737: threshold: 0.738 avg_dist: 0.805 num_edges: 1028 2018-04-11 22:39:06,788 : INFO : epoch 738: threshold: 0.739 avg_dist: 0.804 num_edges: 1020 2018-04-11 22:39:06,798 : INFO : epoch 739: threshold: 0.740 avg_dist: 0.804 num_edges: 1016 2018-04-11 22:39:06,805 : INFO : epoch 740: threshold: 0.741 avg_dist: 0.804 num_edges: 1011 2018-04-11 22:39:06,813 : INFO : epoch 741: threshold: 0.742 avg_dist: 0.805 num_edges: 1003 2018-04-11 22:39:06,821 : INFO : epoch 742: threshold: 0.743 avg_dist: 0.805 num_edges: 997 2018-04-11 22:39:06,828 : INFO : epoch 743: threshold: 0.744 avg_dist: 0.805 num_edges: 993 2018-04-11 22:39:06,836 : INFO : epoch 744: threshold: 0.745 avg_dist: 0.806 num_edges: 991 2018-04-11 22:39:06,850 : INFO : epoch 745: threshold: 0.746 avg_dist: 0.808 num_edges: 985 2018-04-11 22:39:06,858 : INFO : epoch 746: threshold: 0.747 avg_dist: 0.807 num_edges: 979 2018-04-11 22:39:06,865 : INFO : epoch 747: threshold: 0.748 avg_dist: 0.808 num_edges: 974 2018-04-11 22:39:06,871 : INFO : epoch 748: threshold: 0.749 avg_dist: 0.810 num_edges: 971 2018-04-11 22:39:06,879 : INFO : epoch 749: threshold: 0.750 avg_dist: 0.808 num_edges: 963 2018-04-11 22:39:06,886 : INFO : epoch 750: threshold: 0.751 avg_dist: 0.808 num_edges: 960 2018-04-11 22:39:06,894 : INFO : epoch 751: threshold: 0.752 avg_dist: 0.808 num_edges: 953 2018-04-11 22:39:06,901 : INFO : epoch 752: threshold: 0.753 avg_dist: 0.808 num_edges: 946 2018-04-11 22:39:06,909 : INFO : epoch 753: threshold: 0.754 avg_dist: 0.812 num_edges: 943 2018-04-11 22:39:06,916 : INFO : epoch 754: threshold: 0.755 avg_dist: 0.811 num_edges: 940 2018-04-11 22:39:06,923 : INFO : epoch 755: threshold: 0.756 avg_dist: 0.812 num_edges: 935 2018-04-11 22:39:06,932 : INFO : epoch 756: threshold: 0.757 avg_dist: 0.812 num_edges: 933 2018-04-11 22:39:06,939 : INFO : epoch 757: threshold: 0.758 avg_dist: 0.814 num_edges: 921 2018-04-11 22:39:06,951 : INFO : epoch 758: threshold: 0.759 avg_dist: 0.810 num_edges: 913 2018-04-11 22:39:06,960 : INFO : epoch 759: threshold: 0.760 avg_dist: 0.811 num_edges: 907 2018-04-11 22:39:06,967 : INFO : epoch 760: threshold: 0.761 avg_dist: 0.808 num_edges: 904 2018-04-11 22:39:06,974 : INFO : epoch 761: threshold: 0.762 avg_dist: 0.809 num_edges: 900 2018-04-11 22:39:06,983 : INFO : epoch 762: threshold: 0.763 avg_dist: 0.809 num_edges: 895 2018-04-11 22:39:06,990 : INFO : epoch 763: threshold: 0.764 avg_dist: 0.809 num_edges: 892 2018-04-11 22:39:06,999 : INFO : epoch 764: threshold: 0.765 avg_dist: 0.808 num_edges: 884 2018-04-11 22:39:07,007 : INFO : epoch 765: threshold: 0.766 avg_dist: 0.806 num_edges: 878 2018-04-11 22:39:07,015 : INFO : epoch 766: threshold: 0.767 avg_dist: 0.807 num_edges: 873 2018-04-11 22:39:07,024 : INFO : epoch 767: threshold: 0.768 avg_dist: 0.807 num_edges: 868 2018-04-11 22:39:07,036 : INFO : epoch 768: threshold: 0.769 avg_dist: 0.808 num_edges: 859 2018-04-11 22:39:07,044 : INFO : epoch 769: threshold: 0.770 avg_dist: 0.807 num_edges: 856 2018-04-11 22:39:07,051 : INFO : epoch 770: threshold: 0.771 avg_dist: 0.807 num_edges: 856 2018-04-11 22:39:07,058 : INFO : epoch 771: threshold: 0.772 avg_dist: 0.806 num_edges: 849 2018-04-11 22:39:07,065 : INFO : epoch 772: threshold: 0.773 avg_dist: 0.807 num_edges: 842 2018-04-11 22:39:07,073 : INFO : epoch 773: threshold: 0.774 avg_dist: 0.806 num_edges: 841 2018-04-11 22:39:07,085 : INFO : epoch 774: threshold: 0.775 avg_dist: 0.806 num_edges: 835 2018-04-11 22:39:07,093 : INFO : epoch 775: threshold: 0.776 avg_dist: 0.806 num_edges: 827 2018-04-11 22:39:07,100 : INFO : epoch 776: threshold: 0.777 avg_dist: 0.809 num_edges: 823 2018-04-11 22:39:07,107 : INFO : epoch 777: threshold: 0.778 avg_dist: 0.810 num_edges: 815 2018-04-11 22:39:07,114 : INFO : epoch 778: threshold: 0.779 avg_dist: 0.810 num_edges: 813 2018-04-11 22:39:07,124 : INFO : epoch 779: threshold: 0.780 avg_dist: 0.809 num_edges: 803 2018-04-11 22:39:07,132 : INFO : epoch 780: threshold: 0.781 avg_dist: 0.811 num_edges: 799 2018-04-11 22:39:07,139 : INFO : epoch 781: threshold: 0.782 avg_dist: 0.811 num_edges: 791 2018-04-11 22:39:07,147 : INFO : epoch 782: threshold: 0.783 avg_dist: 0.812 num_edges: 782 2018-04-11 22:39:07,155 : INFO : epoch 783: threshold: 0.784 avg_dist: 0.815 num_edges: 778 2018-04-11 22:39:07,162 : INFO : epoch 784: threshold: 0.785 avg_dist: 0.815 num_edges: 775 2018-04-11 22:39:07,171 : INFO : epoch 785: threshold: 0.786 avg_dist: 0.815 num_edges: 769 2018-04-11 22:39:07,180 : INFO : epoch 786: threshold: 0.787 avg_dist: 0.814 num_edges: 764 2018-04-11 22:39:07,187 : INFO : epoch 787: threshold: 0.788 avg_dist: 0.816 num_edges: 759 2018-04-11 22:39:07,195 : INFO : epoch 788: threshold: 0.789 avg_dist: 0.816 num_edges: 756 2018-04-11 22:39:07,203 : INFO : epoch 789: threshold: 0.790 avg_dist: 0.816 num_edges: 753 2018-04-11 22:39:07,214 : INFO : epoch 790: threshold: 0.791 avg_dist: 0.813 num_edges: 746 2018-04-11 22:39:07,222 : INFO : epoch 791: threshold: 0.792 avg_dist: 0.812 num_edges: 742 2018-04-11 22:39:07,230 : INFO : epoch 792: threshold: 0.793 avg_dist: 0.812 num_edges: 737 2018-04-11 22:39:07,237 : INFO : epoch 793: threshold: 0.794 avg_dist: 0.813 num_edges: 728 2018-04-11 22:39:07,248 : INFO : epoch 794: threshold: 0.795 avg_dist: 0.812 num_edges: 721 2018-04-11 22:39:07,255 : INFO : epoch 795: threshold: 0.796 avg_dist: 0.811 num_edges: 715 2018-04-11 22:39:07,263 : INFO : epoch 796: threshold: 0.797 avg_dist: 0.810 num_edges: 709 2018-04-11 22:39:07,271 : INFO : epoch 797: threshold: 0.798 avg_dist: 0.810 num_edges: 703 2018-04-11 22:39:07,278 : INFO : epoch 798: threshold: 0.799 avg_dist: 0.810 num_edges: 699 2018-04-11 22:39:07,290 : INFO : epoch 799: threshold: 0.800 avg_dist: 0.809 num_edges: 696 2018-04-11 22:39:07,297 : INFO : epoch 800: threshold: 0.801 avg_dist: 0.808 num_edges: 690 2018-04-11 22:39:07,305 : INFO : epoch 801: threshold: 0.802 avg_dist: 0.811 num_edges: 682 2018-04-11 22:39:07,313 : INFO : epoch 802: threshold: 0.803 avg_dist: 0.812 num_edges: 678 2018-04-11 22:39:07,323 : INFO : epoch 803: threshold: 0.804 avg_dist: 0.812 num_edges: 672 2018-04-11 22:39:07,329 : INFO : epoch 804: threshold: 0.805 avg_dist: 0.811 num_edges: 670 2018-04-11 22:39:07,336 : INFO : epoch 805: threshold: 0.806 avg_dist: 0.811 num_edges: 663 2018-04-11 22:39:07,344 : INFO : epoch 806: threshold: 0.807 avg_dist: 0.812 num_edges: 659 2018-04-11 22:39:07,350 : INFO : epoch 807: threshold: 0.808 avg_dist: 0.812 num_edges: 652 2018-04-11 22:39:07,357 : INFO : epoch 808: threshold: 0.809 avg_dist: 0.812 num_edges: 647 2018-04-11 22:39:07,370 : INFO : epoch 809: threshold: 0.810 avg_dist: 0.811 num_edges: 641 2018-04-11 22:39:07,378 : INFO : epoch 810: threshold: 0.811 avg_dist: 0.812 num_edges: 636 2018-04-11 22:39:07,387 : INFO : epoch 811: threshold: 0.812 avg_dist: 0.814 num_edges: 632 2018-04-11 22:39:07,396 : INFO : epoch 812: threshold: 0.813 avg_dist: 0.813 num_edges: 628 2018-04-11 22:39:07,404 : INFO : epoch 813: threshold: 0.814 avg_dist: 0.813 num_edges: 624 2018-04-11 22:39:07,415 : INFO : epoch 814: threshold: 0.815 avg_dist: 0.813 num_edges: 616 2018-04-11 22:39:07,422 : INFO : epoch 815: threshold: 0.816 avg_dist: 0.814 num_edges: 610 2018-04-11 22:39:07,430 : INFO : epoch 816: threshold: 0.817 avg_dist: 0.812 num_edges: 603 2018-04-11 22:39:07,437 : INFO : epoch 817: threshold: 0.818 avg_dist: 0.816 num_edges: 597 2018-04-11 22:39:07,444 : INFO : epoch 818: threshold: 0.819 avg_dist: 0.829 num_edges: 591 2018-04-11 22:39:07,455 : INFO : epoch 819: threshold: 0.820 avg_dist: 0.829 num_edges: 586 2018-04-11 22:39:07,465 : INFO : epoch 820: threshold: 0.821 avg_dist: 0.829 num_edges: 585 2018-04-11 22:39:07,474 : INFO : epoch 821: threshold: 0.822 avg_dist: 0.828 num_edges: 581 2018-04-11 22:39:07,486 : INFO : epoch 822: threshold: 0.823 avg_dist: 0.829 num_edges: 576 2018-04-11 22:39:07,498 : INFO : epoch 823: threshold: 0.824 avg_dist: 0.833 num_edges: 569 2018-04-11 22:39:07,511 : INFO : epoch 824: threshold: 0.825 avg_dist: 0.833 num_edges: 566 2018-04-11 22:39:07,520 : INFO : epoch 825: threshold: 0.826 avg_dist: 0.832 num_edges: 559 2018-04-11 22:39:07,528 : INFO : epoch 826: threshold: 0.827 avg_dist: 0.833 num_edges: 554 2018-04-11 22:39:07,539 : INFO : epoch 827: threshold: 0.828 avg_dist: 0.832 num_edges: 549 2018-04-11 22:39:07,550 : INFO : epoch 828: threshold: 0.829 avg_dist: 0.833 num_edges: 544 2018-04-11 22:39:07,558 : INFO : epoch 829: threshold: 0.830 avg_dist: 0.834 num_edges: 540 2018-04-11 22:39:07,568 : INFO : epoch 830: threshold: 0.831 avg_dist: 0.833 num_edges: 535 2018-04-11 22:39:07,577 : INFO : epoch 831: threshold: 0.832 avg_dist: 0.840 num_edges: 528 2018-04-11 22:39:07,586 : INFO : epoch 832: threshold: 0.833 avg_dist: 0.840 num_edges: 521 2018-04-11 22:39:07,596 : INFO : epoch 833: threshold: 0.834 avg_dist: 0.839 num_edges: 519 2018-04-11 22:39:07,604 : INFO : epoch 834: threshold: 0.835 avg_dist: 0.836 num_edges: 516 2018-04-11 22:39:07,612 : INFO : epoch 835: threshold: 0.836 avg_dist: 0.836 num_edges: 514 2018-04-11 22:39:07,619 : INFO : epoch 836: threshold: 0.837 avg_dist: 0.838 num_edges: 510 2018-04-11 22:39:07,628 : INFO : epoch 837: threshold: 0.838 avg_dist: 0.838 num_edges: 510 2018-04-11 22:39:07,638 : INFO : epoch 838: threshold: 0.839 avg_dist: 0.839 num_edges: 509 2018-04-11 22:39:07,647 : INFO : epoch 839: threshold: 0.840 avg_dist: 0.839 num_edges: 508 2018-04-11 22:39:07,655 : INFO : epoch 840: threshold: 0.841 avg_dist: 0.839 num_edges: 506 2018-04-11 22:39:07,665 : INFO : epoch 841: threshold: 0.842 avg_dist: 0.839 num_edges: 505 2018-04-11 22:39:07,673 : INFO : epoch 842: threshold: 0.843 avg_dist: 0.839 num_edges: 502 2018-04-11 22:39:07,684 : INFO : epoch 843: threshold: 0.844 avg_dist: 0.840 num_edges: 496 2018-04-11 22:39:07,693 : INFO : epoch 844: threshold: 0.845 avg_dist: 0.842 num_edges: 492 2018-04-11 22:39:07,701 : INFO : epoch 845: threshold: 0.846 avg_dist: 0.841 num_edges: 489 2018-04-11 22:39:07,709 : INFO : epoch 846: threshold: 0.847 avg_dist: 0.841 num_edges: 488 2018-04-11 22:39:07,723 : INFO : epoch 847: threshold: 0.848 avg_dist: 0.842 num_edges: 483 2018-04-11 22:39:07,733 : INFO : epoch 848: threshold: 0.849 avg_dist: 0.842 num_edges: 478 2018-04-11 22:39:07,742 : INFO : epoch 849: threshold: 0.850 avg_dist: 0.843 num_edges: 474 2018-04-11 22:39:07,754 : INFO : epoch 850: threshold: 0.851 avg_dist: 0.842 num_edges: 470 2018-04-11 22:39:07,767 : INFO : epoch 851: threshold: 0.852 avg_dist: 0.843 num_edges: 467 2018-04-11 22:39:07,774 : INFO : epoch 852: threshold: 0.853 avg_dist: 0.845 num_edges: 461 2018-04-11 22:39:07,785 : INFO : epoch 853: threshold: 0.854 avg_dist: 0.846 num_edges: 453 2018-04-11 22:39:07,794 : INFO : epoch 854: threshold: 0.855 avg_dist: 0.853 num_edges: 450 2018-04-11 22:39:07,803 : INFO : epoch 855: threshold: 0.856 avg_dist: 0.854 num_edges: 445 2018-04-11 22:39:07,810 : INFO : epoch 856: threshold: 0.857 avg_dist: 0.854 num_edges: 439 2018-04-11 22:39:07,819 : INFO : epoch 857: threshold: 0.858 avg_dist: 0.854 num_edges: 432 2018-04-11 22:39:07,828 : INFO : epoch 858: threshold: 0.859 avg_dist: 0.862 num_edges: 427 2018-04-11 22:39:07,836 : INFO : epoch 859: threshold: 0.860 avg_dist: 0.861 num_edges: 421 2018-04-11 22:39:07,844 : INFO : epoch 860: threshold: 0.861 avg_dist: 0.861 num_edges: 416 2018-04-11 22:39:07,854 : INFO : epoch 861: threshold: 0.862 avg_dist: 0.861 num_edges: 414 2018-04-11 22:39:07,861 : INFO : epoch 862: threshold: 0.863 avg_dist: 0.861 num_edges: 412 2018-04-11 22:39:07,869 : INFO : epoch 863: threshold: 0.864 avg_dist: 0.860 num_edges: 405 2018-04-11 22:39:07,876 : INFO : epoch 864: threshold: 0.865 avg_dist: 0.864 num_edges: 399 2018-04-11 22:39:07,886 : INFO : epoch 865: threshold: 0.866 avg_dist: 0.864 num_edges: 396 2018-04-11 22:39:07,896 : INFO : epoch 866: threshold: 0.867 avg_dist: 0.864 num_edges: 393 2018-04-11 22:39:07,907 : INFO : epoch 867: threshold: 0.868 avg_dist: 0.864 num_edges: 385 2018-04-11 22:39:07,914 : INFO : epoch 868: threshold: 0.869 avg_dist: 0.871 num_edges: 377 2018-04-11 22:39:07,921 : INFO : epoch 869: threshold: 0.870 avg_dist: 0.871 num_edges: 373 2018-04-11 22:39:07,930 : INFO : epoch 870: threshold: 0.871 avg_dist: 0.871 num_edges: 367 2018-04-11 22:39:07,937 : INFO : epoch 871: threshold: 0.872 avg_dist: 0.870 num_edges: 364 2018-04-11 22:39:07,945 : INFO : epoch 872: threshold: 0.873 avg_dist: 0.871 num_edges: 359 2018-04-11 22:39:07,953 : INFO : epoch 873: threshold: 0.874 avg_dist: 0.870 num_edges: 355 2018-04-11 22:39:07,960 : INFO : epoch 874: threshold: 0.875 avg_dist: 0.870 num_edges: 350 2018-04-11 22:39:07,966 : INFO : epoch 875: threshold: 0.876 avg_dist: 0.869 num_edges: 345 2018-04-11 22:39:07,974 : INFO : epoch 876: threshold: 0.877 avg_dist: 0.870 num_edges: 342 2018-04-11 22:39:07,981 : INFO : epoch 877: threshold: 0.878 avg_dist: 0.871 num_edges: 335 2018-04-11 22:39:07,990 : INFO : epoch 878: threshold: 0.879 avg_dist: 0.872 num_edges: 327 2018-04-11 22:39:08,002 : INFO : epoch 879: threshold: 0.880 avg_dist: 0.872 num_edges: 323 2018-04-11 22:39:08,010 : INFO : epoch 880: threshold: 0.881 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epoch 902: threshold: 0.903 avg_dist: 0.882 num_edges: 243 2018-04-11 22:39:08,213 : INFO : epoch 903: threshold: 0.904 avg_dist: 0.884 num_edges: 237 2018-04-11 22:39:08,221 : INFO : epoch 904: threshold: 0.905 avg_dist: 0.884 num_edges: 232 2018-04-11 22:39:08,228 : INFO : epoch 905: threshold: 0.906 avg_dist: 0.887 num_edges: 230 2018-04-11 22:39:08,237 : INFO : epoch 906: threshold: 0.907 avg_dist: 0.886 num_edges: 224 2018-04-11 22:39:08,244 : INFO : epoch 907: threshold: 0.908 avg_dist: 0.886 num_edges: 218 2018-04-11 22:39:08,252 : INFO : epoch 908: threshold: 0.909 avg_dist: 0.888 num_edges: 214 2018-04-11 22:39:08,260 : INFO : epoch 909: threshold: 0.910 avg_dist: 0.889 num_edges: 211 2018-04-11 22:39:08,268 : INFO : epoch 910: threshold: 0.911 avg_dist: 0.887 num_edges: 204 2018-04-11 22:39:08,275 : INFO : epoch 911: threshold: 0.912 avg_dist: 0.887 num_edges: 200 2018-04-11 22:39:08,286 : INFO : epoch 912: threshold: 0.913 avg_dist: 0.886 num_edges: 195 2018-04-11 22:39:08,294 : INFO : epoch 913: threshold: 0.914 avg_dist: 0.886 num_edges: 193 2018-04-11 22:39:08,303 : INFO : epoch 914: threshold: 0.915 avg_dist: 0.886 num_edges: 189 2018-04-11 22:39:08,314 : INFO : epoch 915: threshold: 0.916 avg_dist: 0.889 num_edges: 187 2018-04-11 22:39:08,323 : INFO : epoch 916: threshold: 0.917 avg_dist: 0.888 num_edges: 186 2018-04-11 22:39:08,333 : INFO : epoch 917: threshold: 0.918 avg_dist: 0.888 num_edges: 180 2018-04-11 22:39:08,340 : INFO : epoch 918: threshold: 0.919 avg_dist: 0.887 num_edges: 176 2018-04-11 22:39:08,347 : INFO : epoch 919: threshold: 0.920 avg_dist: 0.888 num_edges: 172 2018-04-11 22:39:08,354 : INFO : epoch 920: threshold: 0.921 avg_dist: 0.888 num_edges: 170 2018-04-11 22:39:08,362 : INFO : epoch 921: threshold: 0.922 avg_dist: 0.888 num_edges: 164 2018-04-11 22:39:08,370 : INFO : epoch 922: threshold: 0.923 avg_dist: 0.890 num_edges: 157 2018-04-11 22:39:08,377 : INFO : epoch 923: threshold: 0.924 avg_dist: 0.895 num_edges: 153 2018-04-11 22:39:08,384 : INFO : epoch 924: threshold: 0.925 avg_dist: 0.894 num_edges: 149 2018-04-11 22:39:08,393 : INFO : epoch 925: threshold: 0.926 avg_dist: 0.896 num_edges: 147 2018-04-11 22:39:08,400 : INFO : epoch 926: threshold: 0.927 avg_dist: 0.898 num_edges: 145 2018-04-11 22:39:08,408 : INFO : epoch 927: threshold: 0.928 avg_dist: 0.902 num_edges: 142 2018-04-11 22:39:08,422 : INFO : epoch 928: threshold: 0.929 avg_dist: 0.902 num_edges: 140 2018-04-11 22:39:08,430 : INFO : epoch 929: threshold: 0.930 avg_dist: 0.905 num_edges: 136 2018-04-11 22:39:08,438 : INFO : epoch 930: threshold: 0.931 avg_dist: 0.903 num_edges: 133 2018-04-11 22:39:08,447 : INFO : epoch 931: threshold: 0.932 avg_dist: 0.902 num_edges: 130 2018-04-11 22:39:08,455 : INFO : epoch 932: threshold: 0.933 avg_dist: 0.904 num_edges: 129 2018-04-11 22:39:08,463 : INFO : epoch 933: threshold: 0.934 avg_dist: 0.906 num_edges: 127 2018-04-11 22:39:08,470 : INFO : epoch 934: threshold: 0.935 avg_dist: 0.909 num_edges: 123 2018-04-11 22:39:08,477 : INFO : epoch 935: threshold: 0.936 avg_dist: 0.907 num_edges: 119 2018-04-11 22:39:08,487 : INFO : epoch 936: threshold: 0.937 avg_dist: 0.908 num_edges: 115 2018-04-11 22:39:08,497 : INFO : epoch 937: threshold: 0.938 avg_dist: 0.908 num_edges: 113 2018-04-11 22:39:08,504 : INFO : epoch 938: threshold: 0.939 avg_dist: 0.909 num_edges: 110 2018-04-11 22:39:08,512 : INFO : epoch 939: threshold: 0.940 avg_dist: 0.911 num_edges: 109 2018-04-11 22:39:08,519 : INFO : epoch 940: threshold: 0.941 avg_dist: 0.918 num_edges: 104 2018-04-11 22:39:08,527 : INFO : epoch 941: threshold: 0.942 avg_dist: 0.917 num_edges: 98 2018-04-11 22:39:08,536 : INFO : epoch 942: threshold: 0.943 avg_dist: 0.920 num_edges: 96 2018-04-11 22:39:08,544 : INFO : epoch 943: threshold: 0.944 avg_dist: 0.922 num_edges: 92 2018-04-11 22:39:08,554 : INFO : epoch 944: threshold: 0.945 avg_dist: 0.927 num_edges: 89 2018-04-11 22:39:08,561 : INFO : epoch 945: threshold: 0.946 avg_dist: 0.927 num_edges: 88 2018-04-11 22:39:08,569 : INFO : epoch 946: threshold: 0.947 avg_dist: 0.928 num_edges: 87 2018-04-11 22:39:08,577 : INFO : epoch 947: threshold: 0.948 avg_dist: 0.928 num_edges: 87 2018-04-11 22:39:08,583 : INFO : epoch 948: threshold: 0.949 avg_dist: 0.933 num_edges: 84 2018-04-11 22:39:08,591 : INFO : epoch 949: threshold: 0.950 avg_dist: 0.932 num_edges: 81 2018-04-11 22:39:08,597 : INFO : epoch 950: threshold: 0.951 avg_dist: 0.932 num_edges: 75 2018-04-11 22:39:08,606 : INFO : epoch 951: threshold: 0.952 avg_dist: 0.932 num_edges: 75 2018-04-11 22:39:08,616 : INFO : epoch 952: threshold: 0.953 avg_dist: 0.935 num_edges: 73 2018-04-11 22:39:08,623 : INFO : epoch 953: threshold: 0.954 avg_dist: 0.936 num_edges: 71 2018-04-11 22:39:08,630 : INFO : epoch 954: threshold: 0.955 avg_dist: 0.937 num_edges: 69 2018-04-11 22:39:08,637 : INFO : epoch 955: threshold: 0.956 avg_dist: 0.936 num_edges: 66 2018-04-11 22:39:08,645 : INFO : epoch 956: threshold: 0.957 avg_dist: 0.938 num_edges: 64 2018-04-11 22:39:08,653 : INFO : epoch 957: threshold: 0.958 avg_dist: 0.939 num_edges: 63 2018-04-11 22:39:08,659 : INFO : epoch 958: threshold: 0.959 avg_dist: 0.939 num_edges: 63 2018-04-11 22:39:08,667 : INFO : epoch 959: threshold: 0.960 avg_dist: 0.946 num_edges: 60 2018-04-11 22:39:08,675 : INFO : epoch 960: threshold: 0.961 avg_dist: 0.944 num_edges: 57 2018-04-11 22:39:08,682 : INFO : epoch 961: threshold: 0.962 avg_dist: 0.943 num_edges: 56 2018-04-11 22:39:08,690 : INFO : epoch 962: threshold: 0.963 avg_dist: 0.944 num_edges: 54 2018-04-11 22:39:08,697 : INFO : epoch 963: threshold: 0.964 avg_dist: 0.948 num_edges: 50 2018-04-11 22:39:08,706 : INFO : epoch 964: threshold: 0.965 avg_dist: 0.950 num_edges: 48 2018-04-11 22:39:08,713 : INFO : epoch 965: threshold: 0.966 avg_dist: 0.950 num_edges: 44 2018-04-11 22:39:08,720 : INFO : epoch 966: threshold: 0.967 avg_dist: 0.952 num_edges: 41 2018-04-11 22:39:08,728 : INFO : epoch 967: threshold: 0.968 avg_dist: 0.952 num_edges: 39 2018-04-11 22:39:08,736 : INFO : epoch 968: threshold: 0.969 avg_dist: 0.958 num_edges: 35 2018-04-11 22:39:08,744 : INFO : epoch 969: threshold: 0.970 avg_dist: 0.959 num_edges: 33 2018-04-11 22:39:08,754 : INFO : epoch 970: threshold: 0.971 avg_dist: 0.961 num_edges: 32 2018-04-11 22:39:08,762 : INFO : epoch 971: threshold: 0.972 avg_dist: 0.965 num_edges: 29 2018-04-11 22:39:08,772 : INFO : epoch 972: threshold: 0.973 avg_dist: 0.965 num_edges: 25 2018-04-11 22:39:08,780 : INFO : epoch 973: threshold: 0.974 avg_dist: 0.965 num_edges: 25 2018-04-11 22:39:08,788 : INFO : epoch 974: threshold: 0.975 avg_dist: 0.966 num_edges: 24 2018-04-11 22:39:08,795 : INFO : epoch 975: threshold: 0.976 avg_dist: 0.967 num_edges: 23 2018-04-11 22:39:08,803 : INFO : epoch 976: threshold: 0.977 avg_dist: 0.968 num_edges: 21 2018-04-11 22:39:08,810 : INFO : epoch 977: threshold: 0.978 avg_dist: 0.975 num_edges: 18 2018-04-11 22:39:08,819 : INFO : epoch 978: threshold: 0.979 avg_dist: 0.977 num_edges: 17 2018-04-11 22:39:08,827 : INFO : epoch 979: threshold: 0.980 avg_dist: 0.976 num_edges: 16 2018-04-11 22:39:08,834 : INFO : epoch 980: threshold: 0.981 avg_dist: 0.981 num_edges: 15 2018-04-11 22:39:08,847 : INFO : epoch 981: threshold: 0.982 avg_dist: 0.981 num_edges: 15 2018-04-11 22:39:08,857 : INFO : epoch 982: threshold: 0.983 avg_dist: 0.985 num_edges: 12 2018-04-11 22:39:08,864 : INFO : epoch 983: threshold: 0.984 avg_dist: 0.986 num_edges: 11 2018-04-11 22:39:08,871 : INFO : epoch 984: threshold: 0.985 avg_dist: 0.986 num_edges: 11 2018-04-11 22:39:08,878 : INFO : epoch 985: threshold: 0.986 avg_dist: 0.988 num_edges: 8 2018-04-11 22:39:08,885 : INFO : epoch 986: threshold: 0.987 avg_dist: 0.988 num_edges: 8 2018-04-11 22:39:08,893 : INFO : epoch 987: threshold: 0.988 avg_dist: 0.988 num_edges: 8 2018-04-11 22:39:08,904 : INFO : epoch 988: threshold: 0.989 avg_dist: 0.989 num_edges: 7 2018-04-11 22:39:08,915 : INFO : epoch 989: threshold: 0.990 avg_dist: 0.989 num_edges: 7 2018-04-11 22:39:08,924 : INFO : epoch 990: threshold: 0.991 avg_dist: 0.991 num_edges: 5 2018-04-11 22:39:08,931 : INFO : epoch 991: threshold: 0.992 avg_dist: 0.991 num_edges: 5 2018-04-11 22:39:08,938 : INFO : epoch 992: threshold: 0.993 avg_dist: 0.992 num_edges: 4 2018-04-11 22:39:08,945 : INFO : epoch 993: threshold: 0.994 avg_dist: 0.994 num_edges: 2 2018-04-11 22:39:08,953 : INFO : epoch 994: threshold: 0.995 avg_dist: 0.994 num_edges: 2 2018-04-11 22:39:08,963 : INFO : epoch 995: threshold: 0.996 avg_dist: 0.995 num_edges: 1 2018-04-11 22:39:08,973 : INFO : epoch 996: threshold: 0.997 avg_dist: 0.995 num_edges: 1 2018-04-11 22:39:08,979 : INFO : epoch 997: threshold: 0.998 avg_dist: 0.995 num_edges: 1 2018-04-11 22:39:08,986 : INFO : epoch 998: threshold: 0.999 avg_dist: 0.995 num_edges: 1
threshold: 0.850 dist 0.792538 (723, 0.724) edges 474/471.0 (849)
threshold: 0.850 dist 0.792538 (723, 0.724) edges 474/471.0 (849)
In [51]:
# set edges:
topicnet_adj_mat = set_edges(cosim, 0.850, binary=True)
2018-04-11 22:39:13,346 : INFO : dimension: 195, 195
In [44]:
topicnet_adj_mat.dump(open('./res/topicnetref-20yr_adjmat.pkl','wb'))
Visualise¶
Thresholding:¶
In [52]:
f = threshold_plot(
thresholds,
distances,
edges,
best_threshold,
closest_edges,
best_j_dist_threshold,
lowest_avg_distances,
np.sum(adj_mat)//2,
cosim
)
In [46]:
f.savefig('./IMG/topicnetref20yr_thresholding.png', format='png', bbox_inches='tight')
As series of matrices:¶
In [ ]:
f = plt.figure(figsize=(15, 5))
# ORIGINAL COLALB NET
ax1 = f.add_subplot(131)
ax1.axis('off')
sns.heatmap(adj_mat, cbar=False, square=True)
ax1.set_title('infnet20yr\n(#Edges={})'.format(int(np.sum(adj_mat) // 2)))
# COSIM
ax2 = f.add_subplot(132)
ax2.axis('off')
sns.heatmap(
cosim,
cbar=False,
square=True,
ax=ax2,
vmax=1,
vmin=0,
robust=True,
cmap='gnuplot2')
ax2.set_title('Cosine similairty\nbetween researchers')
# FINAL :
ax3 = f.add_subplot(133)
ax3.axis('off')
sns.heatmap(topicnet_adj_mat, cbar=False, square=True, ax=ax3)
ax3.set_title('topicnetref20yr\n($\epsilon$={:.3f}, #Edges={})'.format(
best_threshold,
int(np.sum(topicnet_adj_mat) // 2)))
f.tight_layout()
In [48]:
f.savefig('./IMG/topicnetref20yr_adjmats.png')
As networks:¶
In [53]:
g_topicnetref20yr = adj_mat_to_graph(topicnet_adj_mat, collabnet.id.tolist())
In [54]:
print(nx.info(g_topicnetref20yr))
Name: Type: Graph Number of nodes: 129 Number of edges: 474 Average degree: 7.3488
In [55]:
artist.draw_default_layout(
g_topicnetref20yr,
lookup_poinf=get_lookup_poinf(),
file_prefix='topicnetref20yr',
SAVE_GRAPHS=True)
2018-04-11 22:39:27,271 : INFO : SAVE_GRAPHS: True
In [56]:
artist.draw_circular_layout(
g_topicnetref20yr,
get_lookup_poinf(),
file_prefix='topicnetref20yr',
SAVE_GRAPHS=False)
2018-04-11 22:39:35,216 : INFO : SAVE_GRAPHS: False
In [57]:
pos = nx.spring_layout(g_topicnetref20yr,k=.1)
In [58]:
# Visualise with topic label
partition_dict = {}
topicnet['list_tm'] = topicnet.topic_probs.apply(lambda x: to_list(x, 100))
n = 0
for i, nodes in enumerate(nx.connected_components(g_topicnetref20yr)):
n += len(nodes)
a = topicnet[topicnet.id.isin(nodes)].list_tm.tolist()
topic_dist = np.mean(a, axis=0)
partition_dict[i] = {
'nodes': nodes,
'topic_dist': topic_dist,
'most_probable_topic': np.argmax(topic_dist)
}
dblp_ = [(80, 'Parallel systems'),
(76, 'Project management'),
(64, 'Bioinformatics'),
(96, 'Cryptography'),
(59, 'IOT'),
(52, '??'),
(69, 'AI'),
(17, 'Social Media'),
(95, 'NLP'),
(8, 'Education (?)'),
(40, 'Logic'),
(36, 'Web (?)'),
(71, 'Probability'),
(23, 'Neural Network'),
(5, 'Modelling'),
(2, 'Agent Based System'),
(43,'Computer network'),
(36, '??'),
(73, '??'),
(15,'Image recognition'),
(57,'Computer vision'),
]
TOPIC_DICT = dict((a,b) for a,b in dblp_)
f = plt.figure(figsize=(20, 12))
ax = f.add_subplot(111)
nx.draw(
g_topicnetref20yr,
pos=pos,
ax=ax,
node_size=50,
alpha=.9,
node_color=artist.color_by_inst(g_topicnetref20yr, get_lookup_poinf()))
for _, node_dict in list(partition_dict.items()):
# calculate average position:
pos_x, pos_y = 0, 0
for node in node_dict['nodes']:
pos_x += pos[node][0]
pos_y += pos[node][1]
pos_x /= len(node_dict['nodes'])
pos_y /= len(node_dict['nodes'])
topic_num = node_dict['most_probable_topic']
ax.text(
pos_x-.1,
pos_y - .1,
"{}-{}".format(topic_num + 1, TOPIC_DICT[topic_num]),
withdash=True,
size=17,
weight='bold')
print((node_dict['most_probable_topic']))
artist.add_inst_labels(ax)
ax.legend(
loc='upper center',
bbox_to_anchor=(0.2, 1.),
ncol=1,
title='institutes',
fontsize=12,
fancybox=True,
shadow=True)
f.tight_layout()
40 80 76 64 96 43 69 71 95 8 8 36 73 15 57 5 2 76
In [ ]:
f.savefig('IMG/topicnetref20yr_w_topic.pdf',format='pdf',bbox_inches='tight')
Descriptive statistics¶
In [ ]:
gccs = generateGCC(g_topicnetref20yr)
In [ ]:
# clustering coefficient
c_coeff,avg = clustering_coeff(g_topicnetref20yr)
print(avg)
print(nx.average_clustering(g_topicnetref20yr,count_zeros=False))
In [ ]:
# transitivity
print(nx.transitivity(g_topicnetref20yr))
In [ ]:
_ = nx.degree_histogram(g_topicnetref20yr)
print(_[0]+_[1])
In [ ]:
nx.degree_histogram(g_topicnetref20yr)
Embedding infnet6yr
Deriving topicnet-6yr¶
In [59]:
# Filter out lookup table according to the tokens
lookup_combined_toks = prepare_toks(2012,2017)
In [60]:
# load collaboration network
collabnet = pd.read_csv(
'../../data/data_schoolofinf/poinf_collabgraph_2012-2017.txt',
sep='\n',
names=['id'])
adj_mat = np.load('../../data/data_schoolofinf/mat/infnet6yrs-adj-mat.order6yr.pkl')
In [61]:
len(collabnet)
Out[61]:
184
In [62]:
# Gather publications for each individuals
topicnet = collabnet.join(df_pubmapping, how='left',on='id')
# generate tokens
topicnet['toks'] = topicnet['pub_ids'].apply(
lambda a: gen_toks(a, lookup_combined_toks))
In [63]:
# load topic model
tm = models.LdaModel.load('../../topicModel/src/tmrest_meta/res/best_ldamodel')
dict_tm = Dictionary.load('../../data/data_schoolofinf/corpora/dictionary.less.meta')
2018-04-11 22:39:54,447 : INFO : loading LdaModel object from ../../topicModel/src/tmrest_meta/res/best_ldamodel 2018-04-11 22:39:54,449 : INFO : loading expElogbeta from ../../topicModel/src/tmrest_meta/res/best_ldamodel.expElogbeta.npy with mmap=None 2018-04-11 22:39:54,451 : INFO : setting ignored attribute dispatcher to None 2018-04-11 22:39:54,452 : INFO : setting ignored attribute state to None 2018-04-11 22:39:54,452 : INFO : setting ignored attribute id2word to None 2018-04-11 22:39:54,453 : INFO : loaded ../../topicModel/src/tmrest_meta/res/best_ldamodel 2018-04-11 22:39:54,454 : INFO : loading LdaModel object from ../../topicModel/src/tmrest_meta/res/best_ldamodel.state 2018-04-11 22:39:54,462 : INFO : loaded ../../topicModel/src/tmrest_meta/res/best_ldamodel.state 2018-04-11 22:39:54,473 : INFO : loading Dictionary object from ../../data/data_schoolofinf/corpora/dictionary.less.meta 2018-04-11 22:39:54,476 : INFO : loaded ../../data/data_schoolofinf/corpora/dictionary.less.meta
In [64]:
# inference step by tm
tm.minimum_probability=0.01
topicnet['topic_probs'] = topicnet.toks.apply(
lambda a: tm.get_document_topics(dict_tm.doc2bow(a)))
In [84]:
# start from 0 to get all the probs
tm.minimum_probability = 0.00
topicnet['topic_probs'] = topicnet.toks.apply(
lambda a: tm.get_document_topics(dict_tm.doc2bow(a)))
ls = ['-', '-.', '--', ':']
# ds = [(5,1),(5,5), (5,10), (5,20)]
marker = ['x', '^', 'd', 'p']
# try values for min probability:
f = plt.figure(figsize=(15, 5))
axes = []
xs = np.linspace(0, tm.num_topics, num=tm.num_topics)
# _xs = np.linspace(xs[0],xs[-1],300)
c = sns.color_palette(n_colors=3)
for i in range(3):
if i == 0:
ax = f.add_subplot(1, 3, i + 1)
else:
ax = f.add_subplot(1, 3, i + 1, sharey=ax)
_probs = np.asarray([a[1] for a in topicnet.topic_probs.iloc[13 + i * 2]])
for j, p in enumerate([0.01, 0.05, 0.1]):
ax.hlines(
p,
xs[0],
xs[-1],
linestyles=':',
linewidth=4,
label='{}'.format(p),
colors=c[j])
# _probs[_probs <= p] = 0
# ax.step(xs, _probs, alpha=0.7, linestyle='-', linewidth=4)
ax.vlines(xs, [0], _probs, alpha=0.7)
# ax.scatter(xs,_probs,,alpha=0.6,s=50,marker=marker[j])
ax.set_yscale('log')
axes.append(ax)
ax.legend().set_visible(False)
h, l = axes[-1].get_legend_handles_labels()
axes[-1].legend(h, l, loc=0)
f.tight_layout()
In [135]:
f.savefig('IMG/try_min_probs.png', format='png', bbox_inches='tight')
In [65]:
# Generate cosine sim between individuals
cosim = compare_researchers(topicnet.topic_probs.tolist(), tm.num_topics)
print(cosim.shape)
(184, 184)
In [66]:
# find the best threshold:
thresholds, edges, distances, closest_edges, best_threshold, lowest_avg_distances, best_j_dist_epoch, best_j_dist_threshold = \
find_best_threshold(adj_mat, cosim, num_iter=10000, step_size=0.001)
2018-04-11 22:40:05,824 : INFO : Number of ground_truth_edges: 361.0 2018-04-11 22:40:05,825 : INFO : binary edges: True 2018-04-11 22:40:05,837 : INFO : epoch 0: threshold: 0.001 avg_dist: 0.977 num_edges: 15929 2018-04-11 22:40:05,849 : INFO : epoch 1: threshold: 0.002 avg_dist: 0.977 num_edges: 15737 2018-04-11 22:40:05,862 : INFO : epoch 2: threshold: 0.003 avg_dist: 0.977 num_edges: 15538 2018-04-11 22:40:05,874 : INFO : epoch 3: threshold: 0.004 avg_dist: 0.976 num_edges: 15364 2018-04-11 22:40:05,887 : INFO : epoch 4: threshold: 0.005 avg_dist: 0.976 num_edges: 15158 2018-04-11 22:40:05,899 : INFO : epoch 5: threshold: 0.006 avg_dist: 0.976 num_edges: 15002 2018-04-11 22:40:05,911 : INFO : epoch 6: threshold: 0.007 avg_dist: 0.976 num_edges: 14858 2018-04-11 22:40:05,921 : INFO : epoch 7: threshold: 0.008 avg_dist: 0.975 num_edges: 14698 2018-04-11 22:40:05,929 : INFO : epoch 8: threshold: 0.009 avg_dist: 0.975 num_edges: 14543 2018-04-11 22:40:05,939 : INFO : epoch 9: threshold: 0.010 avg_dist: 0.975 num_edges: 14417 2018-04-11 22:40:05,947 : INFO : epoch 10: threshold: 0.011 avg_dist: 0.974 num_edges: 14289 2018-04-11 22:40:05,958 : INFO : epoch 11: threshold: 0.012 avg_dist: 0.974 num_edges: 14163 2018-04-11 22:40:05,971 : INFO : epoch 12: threshold: 0.013 avg_dist: 0.974 num_edges: 14036 2018-04-11 22:40:05,979 : INFO : epoch 13: threshold: 0.014 avg_dist: 0.974 num_edges: 13896 2018-04-11 22:40:05,987 : INFO : epoch 14: threshold: 0.015 avg_dist: 0.973 num_edges: 13802 2018-04-11 22:40:05,998 : INFO : epoch 15: threshold: 0.016 avg_dist: 0.973 num_edges: 13688 2018-04-11 22:40:06,007 : INFO : epoch 16: threshold: 0.017 avg_dist: 0.973 num_edges: 13560 2018-04-11 22:40:06,020 : INFO : epoch 17: threshold: 0.018 avg_dist: 0.973 num_edges: 13441 2018-04-11 22:40:06,033 : INFO : epoch 18: threshold: 0.019 avg_dist: 0.972 num_edges: 13347 2018-04-11 22:40:06,041 : INFO : epoch 19: threshold: 0.020 avg_dist: 0.972 num_edges: 13228 2018-04-11 22:40:06,049 : INFO : epoch 20: threshold: 0.021 avg_dist: 0.972 num_edges: 13111 2018-04-11 22:40:06,062 : INFO : epoch 21: threshold: 0.022 avg_dist: 0.971 num_edges: 12984 2018-04-11 22:40:06,075 : INFO : epoch 22: threshold: 0.023 avg_dist: 0.971 num_edges: 12890 2018-04-11 22:40:06,085 : INFO : epoch 23: threshold: 0.024 avg_dist: 0.971 num_edges: 12783 2018-04-11 22:40:06,093 : INFO : epoch 24: threshold: 0.025 avg_dist: 0.970 num_edges: 12672 2018-04-11 22:40:06,104 : INFO : epoch 25: threshold: 0.026 avg_dist: 0.970 num_edges: 12588 2018-04-11 22:40:06,111 : INFO : epoch 26: threshold: 0.027 avg_dist: 0.970 num_edges: 12475 2018-04-11 22:40:06,119 : INFO : epoch 27: threshold: 0.028 avg_dist: 0.969 num_edges: 12354 2018-04-11 22:40:06,128 : INFO : epoch 28: threshold: 0.029 avg_dist: 0.969 num_edges: 12232 2018-04-11 22:40:06,136 : INFO : epoch 29: threshold: 0.030 avg_dist: 0.969 num_edges: 12141 2018-04-11 22:40:06,143 : INFO : epoch 30: threshold: 0.031 avg_dist: 0.968 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: epoch 52: threshold: 0.053 avg_dist: 0.962 num_edges: 10230 2018-04-11 22:40:06,351 : INFO : epoch 53: threshold: 0.054 avg_dist: 0.962 num_edges: 10176 2018-04-11 22:40:06,358 : INFO : epoch 54: threshold: 0.055 avg_dist: 0.961 num_edges: 10111 2018-04-11 22:40:06,367 : INFO : epoch 55: threshold: 0.056 avg_dist: 0.961 num_edges: 10054 2018-04-11 22:40:06,375 : INFO : epoch 56: threshold: 0.057 avg_dist: 0.961 num_edges: 9989 2018-04-11 22:40:06,383 : INFO : epoch 57: threshold: 0.058 avg_dist: 0.960 num_edges: 9920 2018-04-11 22:40:06,389 : INFO : epoch 58: threshold: 0.059 avg_dist: 0.960 num_edges: 9849 2018-04-11 22:40:06,396 : INFO : epoch 59: threshold: 0.060 avg_dist: 0.960 num_edges: 9774 2018-04-11 22:40:06,403 : INFO : epoch 60: threshold: 0.061 avg_dist: 0.959 num_edges: 9707 2018-04-11 22:40:06,414 : INFO : epoch 61: threshold: 0.062 avg_dist: 0.959 num_edges: 9641 2018-04-11 22:40:06,428 : INFO : epoch 62: threshold: 0.063 avg_dist: 0.959 num_edges: 9579 2018-04-11 22:40:06,435 : INFO : epoch 63: threshold: 0.064 avg_dist: 0.958 num_edges: 9517 2018-04-11 22:40:06,443 : INFO : epoch 64: threshold: 0.065 avg_dist: 0.958 num_edges: 9454 2018-04-11 22:40:06,452 : INFO : epoch 65: threshold: 0.066 avg_dist: 0.958 num_edges: 9401 2018-04-11 22:40:06,460 : INFO : epoch 66: threshold: 0.067 avg_dist: 0.957 num_edges: 9335 2018-04-11 22:40:06,472 : INFO : epoch 67: threshold: 0.068 avg_dist: 0.957 num_edges: 9271 2018-04-11 22:40:06,480 : INFO : epoch 68: threshold: 0.069 avg_dist: 0.957 num_edges: 9205 2018-04-11 22:40:06,489 : INFO : epoch 69: threshold: 0.070 avg_dist: 0.956 num_edges: 9150 2018-04-11 22:40:06,497 : INFO : epoch 70: threshold: 0.071 avg_dist: 0.956 num_edges: 9087 2018-04-11 22:40:06,504 : INFO : epoch 71: threshold: 0.072 avg_dist: 0.956 num_edges: 9024 2018-04-11 22:40:06,511 : INFO : epoch 72: threshold: 0.073 avg_dist: 0.955 num_edges: 8971 2018-04-11 22:40:06,518 : INFO : epoch 73: threshold: 0.074 avg_dist: 0.955 num_edges: 8907 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num_edges: 1083 2018-04-11 22:40:10,997 : INFO : epoch 601: threshold: 0.602 avg_dist: 0.801 num_edges: 1081 2018-04-11 22:40:11,005 : INFO : epoch 602: threshold: 0.603 avg_dist: 0.800 num_edges: 1079 2018-04-11 22:40:11,014 : INFO : epoch 603: threshold: 0.604 avg_dist: 0.802 num_edges: 1076 2018-04-11 22:40:11,021 : INFO : epoch 604: threshold: 0.605 avg_dist: 0.802 num_edges: 1074 2018-04-11 22:40:11,029 : INFO : epoch 605: threshold: 0.606 avg_dist: 0.803 num_edges: 1070 2018-04-11 22:40:11,037 : INFO : epoch 606: threshold: 0.607 avg_dist: 0.802 num_edges: 1059 2018-04-11 22:40:11,045 : INFO : epoch 607: threshold: 0.608 avg_dist: 0.802 num_edges: 1055 2018-04-11 22:40:11,053 : INFO : epoch 608: threshold: 0.609 avg_dist: 0.801 num_edges: 1051 2018-04-11 22:40:11,059 : INFO : epoch 609: threshold: 0.610 avg_dist: 0.801 num_edges: 1046 2018-04-11 22:40:11,066 : INFO : epoch 610: threshold: 0.611 avg_dist: 0.801 num_edges: 1044 2018-04-11 22:40:11,074 : INFO : epoch 611: threshold: 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epoch 622: threshold: 0.623 avg_dist: 0.800 num_edges: 1000 2018-04-11 22:40:11,166 : INFO : epoch 623: threshold: 0.624 avg_dist: 0.800 num_edges: 998 2018-04-11 22:40:11,173 : INFO : epoch 624: threshold: 0.625 avg_dist: 0.800 num_edges: 994 2018-04-11 22:40:11,180 : INFO : epoch 625: threshold: 0.626 avg_dist: 0.799 num_edges: 991 2018-04-11 22:40:11,187 : INFO : epoch 626: threshold: 0.627 avg_dist: 0.798 num_edges: 986 2018-04-11 22:40:11,195 : INFO : epoch 627: threshold: 0.628 avg_dist: 0.798 num_edges: 983 2018-04-11 22:40:11,203 : INFO : epoch 628: threshold: 0.629 avg_dist: 0.798 num_edges: 981 2018-04-11 22:40:11,211 : INFO : epoch 629: threshold: 0.630 avg_dist: 0.798 num_edges: 978 2018-04-11 22:40:11,219 : INFO : epoch 630: threshold: 0.631 avg_dist: 0.799 num_edges: 973 2018-04-11 22:40:11,225 : INFO : epoch 631: threshold: 0.632 avg_dist: 0.798 num_edges: 968 2018-04-11 22:40:11,232 : INFO : epoch 632: threshold: 0.633 avg_dist: 0.797 num_edges: 964 2018-04-11 22:40:11,240 : INFO : epoch 633: threshold: 0.634 avg_dist: 0.797 num_edges: 962 2018-04-11 22:40:11,251 : INFO : epoch 634: threshold: 0.635 avg_dist: 0.796 num_edges: 958 2018-04-11 22:40:11,260 : INFO : epoch 635: threshold: 0.636 avg_dist: 0.796 num_edges: 957 2018-04-11 22:40:11,267 : INFO : epoch 636: threshold: 0.637 avg_dist: 0.796 num_edges: 955 2018-04-11 22:40:11,273 : INFO : epoch 637: threshold: 0.638 avg_dist: 0.795 num_edges: 951 2018-04-11 22:40:11,280 : INFO : epoch 638: threshold: 0.639 avg_dist: 0.794 num_edges: 948 2018-04-11 22:40:11,287 : INFO : epoch 639: threshold: 0.640 avg_dist: 0.795 num_edges: 945 2018-04-11 22:40:11,294 : INFO : epoch 640: threshold: 0.641 avg_dist: 0.794 num_edges: 934 2018-04-11 22:40:11,301 : INFO : epoch 641: threshold: 0.642 avg_dist: 0.794 num_edges: 933 2018-04-11 22:40:11,309 : INFO : epoch 642: threshold: 0.643 avg_dist: 0.793 num_edges: 927 2018-04-11 22:40:11,320 : INFO : epoch 643: threshold: 0.644 avg_dist: 0.792 num_edges: 925 2018-04-11 22:40:11,328 : INFO : epoch 644: threshold: 0.645 avg_dist: 0.792 num_edges: 923 2018-04-11 22:40:11,335 : INFO : epoch 645: threshold: 0.646 avg_dist: 0.791 num_edges: 920 2018-04-11 22:40:11,343 : INFO : epoch 646: threshold: 0.647 avg_dist: 0.792 num_edges: 915 2018-04-11 22:40:11,350 : INFO : epoch 647: threshold: 0.648 avg_dist: 0.792 num_edges: 910 2018-04-11 22:40:11,357 : INFO : epoch 648: threshold: 0.649 avg_dist: 0.791 num_edges: 905 2018-04-11 22:40:11,364 : INFO : epoch 649: threshold: 0.650 avg_dist: 0.790 num_edges: 900 2018-04-11 22:40:11,370 : INFO : epoch 650: threshold: 0.651 avg_dist: 0.788 num_edges: 891 2018-04-11 22:40:11,378 : INFO : epoch 651: threshold: 0.652 avg_dist: 0.787 num_edges: 884 2018-04-11 22:40:11,386 : INFO : epoch 652: threshold: 0.653 avg_dist: 0.786 num_edges: 876 2018-04-11 22:40:11,394 : INFO : epoch 653: threshold: 0.654 avg_dist: 0.786 num_edges: 871 2018-04-11 22:40:11,403 : INFO : epoch 654: threshold: 0.655 avg_dist: 0.785 num_edges: 869 2018-04-11 22:40:11,419 : INFO : epoch 655: threshold: 0.656 avg_dist: 0.784 num_edges: 864 2018-04-11 22:40:11,432 : INFO : epoch 656: threshold: 0.657 avg_dist: 0.780 num_edges: 860 2018-04-11 22:40:11,443 : INFO : epoch 657: threshold: 0.658 avg_dist: 0.779 num_edges: 855 2018-04-11 22:40:11,457 : INFO : epoch 658: threshold: 0.659 avg_dist: 0.781 num_edges: 849 2018-04-11 22:40:11,471 : INFO : epoch 659: threshold: 0.660 avg_dist: 0.780 num_edges: 845 2018-04-11 22:40:11,484 : INFO : epoch 660: threshold: 0.661 avg_dist: 0.779 num_edges: 839 2018-04-11 22:40:11,491 : INFO : epoch 661: threshold: 0.662 avg_dist: 0.778 num_edges: 837 2018-04-11 22:40:11,498 : INFO : epoch 662: threshold: 0.663 avg_dist: 0.777 num_edges: 833 2018-04-11 22:40:11,505 : INFO : epoch 663: threshold: 0.664 avg_dist: 0.777 num_edges: 831 2018-04-11 22:40:11,512 : INFO : epoch 664: threshold: 0.665 avg_dist: 0.776 num_edges: 826 2018-04-11 22:40:11,519 : INFO : epoch 665: threshold: 0.666 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epoch 687: threshold: 0.688 avg_dist: 0.773 num_edges: 753 2018-04-11 22:40:11,705 : INFO : epoch 688: threshold: 0.689 avg_dist: 0.774 num_edges: 747 2018-04-11 22:40:11,712 : INFO : epoch 689: threshold: 0.690 avg_dist: 0.773 num_edges: 744 2018-04-11 22:40:11,718 : INFO : epoch 690: threshold: 0.691 avg_dist: 0.775 num_edges: 742 2018-04-11 22:40:11,725 : INFO : epoch 691: threshold: 0.692 avg_dist: 0.775 num_edges: 742 2018-04-11 22:40:11,733 : INFO : epoch 692: threshold: 0.693 avg_dist: 0.777 num_edges: 737 2018-04-11 22:40:11,740 : INFO : epoch 693: threshold: 0.694 avg_dist: 0.780 num_edges: 733 2018-04-11 22:40:11,747 : INFO : epoch 694: threshold: 0.695 avg_dist: 0.780 num_edges: 730 2018-04-11 22:40:11,756 : INFO : epoch 695: threshold: 0.696 avg_dist: 0.778 num_edges: 725 2018-04-11 22:40:11,763 : INFO : epoch 696: threshold: 0.697 avg_dist: 0.776 num_edges: 720 2018-04-11 22:40:11,771 : INFO : epoch 697: threshold: 0.698 avg_dist: 0.776 num_edges: 717 2018-04-11 22:40:11,778 : INFO : epoch 698: threshold: 0.699 avg_dist: 0.775 num_edges: 711 2018-04-11 22:40:11,791 : INFO : epoch 699: threshold: 0.700 avg_dist: 0.774 num_edges: 708 2018-04-11 22:40:11,798 : INFO : epoch 700: threshold: 0.701 avg_dist: 0.774 num_edges: 708 2018-04-11 22:40:11,806 : INFO : epoch 701: threshold: 0.702 avg_dist: 0.774 num_edges: 706 2018-04-11 22:40:11,820 : INFO : epoch 702: threshold: 0.703 avg_dist: 0.773 num_edges: 704 2018-04-11 22:40:11,828 : INFO : epoch 703: threshold: 0.704 avg_dist: 0.774 num_edges: 702 2018-04-11 22:40:11,840 : INFO : epoch 704: threshold: 0.705 avg_dist: 0.774 num_edges: 700 2018-04-11 22:40:11,847 : INFO : epoch 705: threshold: 0.706 avg_dist: 0.773 num_edges: 695 2018-04-11 22:40:11,854 : INFO : epoch 706: threshold: 0.707 avg_dist: 0.773 num_edges: 692 2018-04-11 22:40:11,862 : INFO : epoch 707: threshold: 0.708 avg_dist: 0.771 num_edges: 687 2018-04-11 22:40:11,875 : INFO : epoch 708: threshold: 0.709 avg_dist: 0.771 num_edges: 684 2018-04-11 22:40:11,881 : INFO : epoch 709: threshold: 0.710 avg_dist: 0.770 num_edges: 681 2018-04-11 22:40:11,888 : INFO : epoch 710: threshold: 0.711 avg_dist: 0.769 num_edges: 676 2018-04-11 22:40:11,895 : INFO : epoch 711: threshold: 0.712 avg_dist: 0.769 num_edges: 674 2018-04-11 22:40:11,903 : INFO : epoch 712: threshold: 0.713 avg_dist: 0.770 num_edges: 671 2018-04-11 22:40:11,909 : INFO : epoch 713: threshold: 0.714 avg_dist: 0.770 num_edges: 670 2018-04-11 22:40:11,916 : INFO : epoch 714: threshold: 0.715 avg_dist: 0.772 num_edges: 661 2018-04-11 22:40:11,924 : INFO : epoch 715: threshold: 0.716 avg_dist: 0.776 num_edges: 652 2018-04-11 22:40:11,933 : INFO : epoch 716: threshold: 0.717 avg_dist: 0.774 num_edges: 649 2018-04-11 22:40:11,947 : INFO : epoch 717: threshold: 0.718 avg_dist: 0.775 num_edges: 643 2018-04-11 22:40:11,955 : INFO : epoch 718: threshold: 0.719 avg_dist: 0.774 num_edges: 640 2018-04-11 22:40:11,969 : INFO : epoch 719: threshold: 0.720 avg_dist: 0.774 num_edges: 638 2018-04-11 22:40:11,980 : INFO : epoch 720: threshold: 0.721 avg_dist: 0.774 num_edges: 635 2018-04-11 22:40:11,992 : INFO : epoch 721: threshold: 0.722 avg_dist: 0.774 num_edges: 635 2018-04-11 22:40:12,002 : INFO : epoch 722: threshold: 0.723 avg_dist: 0.776 num_edges: 630 2018-04-11 22:40:12,010 : INFO : epoch 723: threshold: 0.724 avg_dist: 0.776 num_edges: 627 2018-04-11 22:40:12,018 : INFO : epoch 724: threshold: 0.725 avg_dist: 0.776 num_edges: 621 2018-04-11 22:40:12,025 : INFO : epoch 725: threshold: 0.726 avg_dist: 0.776 num_edges: 619 2018-04-11 22:40:12,034 : INFO : epoch 726: threshold: 0.727 avg_dist: 0.775 num_edges: 617 2018-04-11 22:40:12,040 : INFO : epoch 727: threshold: 0.728 avg_dist: 0.773 num_edges: 614 2018-04-11 22:40:12,047 : INFO : epoch 728: threshold: 0.729 avg_dist: 0.774 num_edges: 609 2018-04-11 22:40:12,054 : INFO : epoch 729: threshold: 0.730 avg_dist: 0.773 num_edges: 602 2018-04-11 22:40:12,062 : INFO : epoch 730: threshold: 0.731 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epoch 752: threshold: 0.753 avg_dist: 0.784 num_edges: 535 2018-04-11 22:40:12,271 : INFO : epoch 753: threshold: 0.754 avg_dist: 0.787 num_edges: 530 2018-04-11 22:40:12,278 : INFO : epoch 754: threshold: 0.755 avg_dist: 0.788 num_edges: 525 2018-04-11 22:40:12,286 : INFO : epoch 755: threshold: 0.756 avg_dist: 0.787 num_edges: 519 2018-04-11 22:40:12,293 : INFO : epoch 756: threshold: 0.757 avg_dist: 0.784 num_edges: 516 2018-04-11 22:40:12,302 : INFO : epoch 757: threshold: 0.758 avg_dist: 0.783 num_edges: 509 2018-04-11 22:40:12,310 : INFO : epoch 758: threshold: 0.759 avg_dist: 0.782 num_edges: 508 2018-04-11 22:40:12,322 : INFO : epoch 759: threshold: 0.760 avg_dist: 0.781 num_edges: 506 2018-04-11 22:40:12,331 : INFO : epoch 760: threshold: 0.761 avg_dist: 0.780 num_edges: 503 2018-04-11 22:40:12,338 : INFO : epoch 761: threshold: 0.762 avg_dist: 0.784 num_edges: 501 2018-04-11 22:40:12,346 : INFO : epoch 762: threshold: 0.763 avg_dist: 0.784 num_edges: 500 2018-04-11 22:40:12,355 : INFO : epoch 763: threshold: 0.764 avg_dist: 0.784 num_edges: 499 2018-04-11 22:40:12,363 : INFO : epoch 764: threshold: 0.765 avg_dist: 0.787 num_edges: 495 2018-04-11 22:40:12,370 : INFO : epoch 765: threshold: 0.766 avg_dist: 0.789 num_edges: 494 2018-04-11 22:40:12,377 : INFO : epoch 766: threshold: 0.767 avg_dist: 0.789 num_edges: 494 2018-04-11 22:40:12,384 : INFO : epoch 767: threshold: 0.768 avg_dist: 0.786 num_edges: 488 2018-04-11 22:40:12,391 : INFO : epoch 768: threshold: 0.769 avg_dist: 0.785 num_edges: 486 2018-04-11 22:40:12,400 : INFO : epoch 769: threshold: 0.770 avg_dist: 0.785 num_edges: 484 2018-04-11 22:40:12,407 : INFO : epoch 770: threshold: 0.771 avg_dist: 0.785 num_edges: 483 2018-04-11 22:40:12,414 : INFO : epoch 771: threshold: 0.772 avg_dist: 0.788 num_edges: 481 2018-04-11 22:40:12,423 : INFO : epoch 772: threshold: 0.773 avg_dist: 0.787 num_edges: 480 2018-04-11 22:40:12,430 : INFO : epoch 773: threshold: 0.774 avg_dist: 0.787 num_edges: 474 2018-04-11 22:40:12,438 : INFO : epoch 774: threshold: 0.775 avg_dist: 0.787 num_edges: 473 2018-04-11 22:40:12,445 : INFO : epoch 775: threshold: 0.776 avg_dist: 0.786 num_edges: 471 2018-04-11 22:40:12,454 : INFO : epoch 776: threshold: 0.777 avg_dist: 0.786 num_edges: 469 2018-04-11 22:40:12,461 : INFO : epoch 777: threshold: 0.778 avg_dist: 0.782 num_edges: 465 2018-04-11 22:40:12,469 : INFO : epoch 778: threshold: 0.779 avg_dist: 0.782 num_edges: 465 2018-04-11 22:40:12,475 : INFO : epoch 779: threshold: 0.780 avg_dist: 0.785 num_edges: 461 2018-04-11 22:40:12,486 : INFO : epoch 780: threshold: 0.781 avg_dist: 0.785 num_edges: 461 2018-04-11 22:40:12,495 : INFO : epoch 781: threshold: 0.782 avg_dist: 0.784 num_edges: 458 2018-04-11 22:40:12,502 : INFO : epoch 782: threshold: 0.783 avg_dist: 0.784 num_edges: 458 2018-04-11 22:40:12,509 : INFO : epoch 783: threshold: 0.784 avg_dist: 0.784 num_edges: 457 2018-04-11 22:40:12,515 : INFO : epoch 784: threshold: 0.785 avg_dist: 0.785 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2018-04-11 22:40:12,971 : INFO : epoch 839: threshold: 0.840 avg_dist: 0.801 num_edges: 323 2018-04-11 22:40:12,978 : INFO : epoch 840: threshold: 0.841 avg_dist: 0.811 num_edges: 321 2018-04-11 22:40:12,990 : INFO : epoch 841: threshold: 0.842 avg_dist: 0.811 num_edges: 319 2018-04-11 22:40:13,002 : INFO : epoch 842: threshold: 0.843 avg_dist: 0.813 num_edges: 317 2018-04-11 22:40:13,014 : INFO : epoch 843: threshold: 0.844 avg_dist: 0.810 num_edges: 312 2018-04-11 22:40:13,020 : INFO : epoch 844: threshold: 0.845 avg_dist: 0.810 num_edges: 312 2018-04-11 22:40:13,030 : INFO : epoch 845: threshold: 0.846 avg_dist: 0.810 num_edges: 311 2018-04-11 22:40:13,036 : INFO : epoch 846: threshold: 0.847 avg_dist: 0.809 num_edges: 310 2018-04-11 22:40:13,044 : INFO : epoch 847: threshold: 0.848 avg_dist: 0.808 num_edges: 307 2018-04-11 22:40:13,051 : INFO : epoch 848: threshold: 0.849 avg_dist: 0.813 num_edges: 305 2018-04-11 22:40:13,058 : INFO : epoch 849: threshold: 0.850 avg_dist: 0.812 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epoch 882: threshold: 0.883 avg_dist: 0.830 num_edges: 250 2018-04-11 22:40:13,318 : INFO : epoch 883: threshold: 0.884 avg_dist: 0.830 num_edges: 250 2018-04-11 22:40:13,326 : INFO : epoch 884: threshold: 0.885 avg_dist: 0.830 num_edges: 250 2018-04-11 22:40:13,333 : INFO : epoch 885: threshold: 0.886 avg_dist: 0.829 num_edges: 247 2018-04-11 22:40:13,340 : INFO : epoch 886: threshold: 0.887 avg_dist: 0.830 num_edges: 244 2018-04-11 22:40:13,346 : INFO : epoch 887: threshold: 0.888 avg_dist: 0.837 num_edges: 241 2018-04-11 22:40:13,353 : INFO : epoch 888: threshold: 0.889 avg_dist: 0.837 num_edges: 238 2018-04-11 22:40:13,360 : INFO : epoch 889: threshold: 0.890 avg_dist: 0.839 num_edges: 237 2018-04-11 22:40:13,367 : INFO : epoch 890: threshold: 0.891 avg_dist: 0.839 num_edges: 237 2018-04-11 22:40:13,374 : INFO : epoch 891: threshold: 0.892 avg_dist: 0.842 num_edges: 234 2018-04-11 22:40:13,381 : INFO : epoch 892: threshold: 0.893 avg_dist: 0.842 num_edges: 231 2018-04-11 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epoch 947: threshold: 0.948 avg_dist: 0.882 num_edges: 115 2018-04-11 22:40:13,837 : INFO : epoch 948: threshold: 0.949 avg_dist: 0.882 num_edges: 113 2018-04-11 22:40:13,843 : INFO : epoch 949: threshold: 0.950 avg_dist: 0.884 num_edges: 109 2018-04-11 22:40:13,852 : INFO : epoch 950: threshold: 0.951 avg_dist: 0.884 num_edges: 104 2018-04-11 22:40:13,858 : INFO : epoch 951: threshold: 0.952 avg_dist: 0.884 num_edges: 101 2018-04-11 22:40:13,866 : INFO : epoch 952: threshold: 0.953 avg_dist: 0.884 num_edges: 99 2018-04-11 22:40:13,874 : INFO : epoch 953: threshold: 0.954 avg_dist: 0.898 num_edges: 94 2018-04-11 22:40:13,883 : INFO : epoch 954: threshold: 0.955 avg_dist: 0.898 num_edges: 91 2018-04-11 22:40:13,889 : INFO : epoch 955: threshold: 0.956 avg_dist: 0.897 num_edges: 89 2018-04-11 22:40:13,896 : INFO : epoch 956: threshold: 0.957 avg_dist: 0.896 num_edges: 88 2018-04-11 22:40:13,904 : INFO : epoch 957: threshold: 0.958 avg_dist: 0.898 num_edges: 83 2018-04-11 22:40:13,911 : INFO : epoch 958: threshold: 0.959 avg_dist: 0.899 num_edges: 82 2018-04-11 22:40:13,923 : INFO : epoch 959: threshold: 0.960 avg_dist: 0.902 num_edges: 81 2018-04-11 22:40:13,931 : INFO : epoch 960: threshold: 0.961 avg_dist: 0.902 num_edges: 79 2018-04-11 22:40:13,938 : INFO : epoch 961: threshold: 0.962 avg_dist: 0.902 num_edges: 79 2018-04-11 22:40:13,944 : INFO : epoch 962: threshold: 0.963 avg_dist: 0.904 num_edges: 76 2018-04-11 22:40:13,954 : INFO : epoch 963: threshold: 0.964 avg_dist: 0.905 num_edges: 73 2018-04-11 22:40:13,961 : INFO : epoch 964: threshold: 0.965 avg_dist: 0.911 num_edges: 70 2018-04-11 22:40:13,973 : INFO : epoch 965: threshold: 0.966 avg_dist: 0.918 num_edges: 67 2018-04-11 22:40:13,980 : INFO : epoch 966: threshold: 0.967 avg_dist: 0.933 num_edges: 61 2018-04-11 22:40:13,989 : INFO : epoch 967: threshold: 0.968 avg_dist: 0.934 num_edges: 60 2018-04-11 22:40:13,996 : INFO : epoch 968: threshold: 0.969 avg_dist: 0.939 num_edges: 58 2018-04-11 22:40:14,007 : INFO : epoch 969: threshold: 0.970 avg_dist: 0.940 num_edges: 57 2018-04-11 22:40:14,014 : INFO : epoch 970: threshold: 0.971 avg_dist: 0.940 num_edges: 54 2018-04-11 22:40:14,022 : INFO : epoch 971: threshold: 0.972 avg_dist: 0.941 num_edges: 51 2018-04-11 22:40:14,028 : INFO : epoch 972: threshold: 0.973 avg_dist: 0.941 num_edges: 49 2018-04-11 22:40:14,034 : INFO : epoch 973: threshold: 0.974 avg_dist: 0.941 num_edges: 48 2018-04-11 22:40:14,041 : INFO : epoch 974: threshold: 0.975 avg_dist: 0.944 num_edges: 46 2018-04-11 22:40:14,048 : INFO : epoch 975: threshold: 0.976 avg_dist: 0.947 num_edges: 43 2018-04-11 22:40:14,057 : INFO : epoch 976: threshold: 0.977 avg_dist: 0.946 num_edges: 42 2018-04-11 22:40:14,064 : INFO : epoch 977: threshold: 0.978 avg_dist: 0.946 num_edges: 41 2018-04-11 22:40:14,071 : INFO : epoch 978: threshold: 0.979 avg_dist: 0.951 num_edges: 38 2018-04-11 22:40:14,081 : INFO : epoch 979: threshold: 0.980 avg_dist: 0.955 num_edges: 35 2018-04-11 22:40:14,100 : INFO : epoch 980: threshold: 0.981 avg_dist: 0.958 num_edges: 31 2018-04-11 22:40:14,113 : INFO : epoch 981: threshold: 0.982 avg_dist: 0.958 num_edges: 28 2018-04-11 22:40:14,122 : INFO : epoch 982: threshold: 0.983 avg_dist: 0.957 num_edges: 26 2018-04-11 22:40:14,134 : INFO : epoch 983: threshold: 0.984 avg_dist: 0.958 num_edges: 22 2018-04-11 22:40:14,145 : INFO : epoch 984: threshold: 0.985 avg_dist: 0.956 num_edges: 21 2018-04-11 22:40:14,153 : INFO : epoch 985: threshold: 0.986 avg_dist: 0.960 num_edges: 19 2018-04-11 22:40:14,161 : INFO : epoch 986: threshold: 0.987 avg_dist: 0.960 num_edges: 19 2018-04-11 22:40:14,168 : INFO : epoch 987: threshold: 0.988 avg_dist: 0.965 num_edges: 17 2018-04-11 22:40:14,182 : INFO : epoch 988: threshold: 0.989 avg_dist: 0.965 num_edges: 17 2018-04-11 22:40:14,189 : INFO : epoch 989: threshold: 0.990 avg_dist: 0.971 num_edges: 16 2018-04-11 22:40:14,196 : INFO : epoch 990: threshold: 0.991 avg_dist: 0.971 num_edges: 16 2018-04-11 22:40:14,208 : INFO : epoch 991: threshold: 0.992 avg_dist: 0.978 num_edges: 11 2018-04-11 22:40:14,215 : INFO : epoch 992: threshold: 0.993 avg_dist: 0.975 num_edges: 10 2018-04-11 22:40:14,224 : INFO : epoch 993: threshold: 0.994 avg_dist: 0.976 num_edges: 8 2018-04-11 22:40:14,231 : INFO : epoch 994: threshold: 0.995 avg_dist: 0.982 num_edges: 5 2018-04-11 22:40:14,239 : INFO : epoch 995: threshold: 0.996 avg_dist: 0.984 num_edges: 4 2018-04-11 22:40:14,246 : INFO : epoch 996: threshold: 0.997 avg_dist: 0.988 num_edges: 2 2018-04-11 22:40:14,254 : INFO : epoch 997: threshold: 0.998 avg_dist: 0.995 num_edges: 1 2018-04-11 22:40:14,263 : INFO : epoch 998: threshold: 0.999 avg_dist: 0.995 num_edges: 1
threshold: 0.826 dist 0.768510 (711, 0.712) edges 361/361.0 (825)
threshold: 0.826 dist 0.768510 (711, 0.712) edges 361/361.0 (825)
In [67]:
# Set the edges:
topicnet_adj_mat = set_edges(cosim, 0.826, binary=True)
topicnet_adj_mat.dump('./res/topicnet6yr_adjmat.pkl')
2018-04-11 22:40:15,027 : INFO : dimension: 184, 184
Visualise¶
Thresholding:¶
In [68]:
f = threshold_plot(thresholds, distances, edges, best_threshold, closest_edges,
best_j_dist_threshold, lowest_avg_distances,
np.sum(adj_mat) // 2, cosim)
In [ ]:
f.savefig('./IMG/topicnet6yr_thresholding.png', format='png', bbox_inches='tight')
As series of matrices:¶
In [ ]:
# ORIGINAL COLALB NET
f1 = plt.figure(figsize=(8, 8))
ax1 = f1.add_subplot(111)
ax1.axis('off')
sns.heatmap(adj_mat, cbar=False, square=True)
# ax1.set_title('infnet6yr\n(#Edges={})'.format(int(np.sum(adj_mat) // 2)))
# COSIM
f2 = plt.figure(figsize=(8, 8))
ax2 = f2.add_subplot(111)
ax2.axis('off')
sns.heatmap(
cosim,
cbar=False,
square=True,
ax=ax2,
vmax=1,
vmin=0,
robust=True,
cmap='gnuplot2')
# ax2.set_title('Cosine similairty\nbetween researchers')
# FINAL :
f3 = plt.figure(figsize=(8, 8))
ax3 = f3.add_subplot(111)
ax3.axis('off')
sns.heatmap(topicnet_adj_mat, cbar=False, square=True, ax=ax3)
# ax3.set_title('topicnet6yr\n($\epsilon$={:.3f}, #Edges={})'.format(
# best_threshold,
# int(np.sum(topicnet_adj_mat) // 2)))
In [ ]:
f1.savefig('./IMG/topicnet6yr_adjmat_org.png')
f2.savefig('./IMG/topicnet6yr_adjmat_cosim.png')
f3.savefig('./IMG/topicnet6yr_adjmat_topicnet.png')
In [ ]:
jaccard_adj_mat = set_edges(cosim, best_j_dist_threshold, binary=True)
# jaccard matrix
f4 = plt.figure(figsize=(8, 8))
ax4 = f4.add_subplot(111)
ax4.axis('off')
sns.heatmap(jaccard_adj_mat, cbar=False, square=True, ax=ax4)
In [ ]:
f4.savefig('./IMG/topicnet6yr_adjmat_jaccard.png')
As networks:¶
In [69]:
g_topicnet6yr = adj_mat_to_graph(topicnet_adj_mat, collabnet.id.tolist())
In [70]:
print(nx.info(g_topicnet6yr))
Name: Type: Graph Number of nodes: 152 Number of edges: 361 Average degree: 4.7500
In [71]:
artist.draw_default_layout(
g_topicnet6yr,
lookup_poinf=get_lookup_poinf(),
file_prefix='topicnet6yr',
SAVE_GRAPHS=False)
2018-04-11 22:40:44,635 : INFO : SAVE_GRAPHS: False
In [72]:
artist.draw_circular_layout(
g_topicnet6yr,
get_lookup_poinf(),
file_prefix='topicnet6yr',
SAVE_GRAPHS=False)
2018-04-11 22:40:46,752 : INFO : SAVE_GRAPHS: False
Descriptive statistics¶
In [74]:
partition_dict = {}
topicnet['list_tm'] = topicnet.topic_probs.apply(lambda x: to_list(x, 30))
n = 0
for i, nodes in enumerate(nx.connected_components(g_topicnet6yr)):
n += len(nodes)
a = topicnet[topicnet.id.isin(nodes)].list_tm.tolist()
topic_dist = np.mean(a, axis=0)
partition_dict[i] = {
'nodes': nodes,
'topic_dist': topic_dist,
'most_probable_topic': np.argmax(topic_dist)
}
In [75]:
_pos = nx.spring_layout(g_topicnet6yr,k=.12, iterations=60)
In [76]:
topics= [19, 21, 27, 7, 28, 8,
0, 23, 13, 10, 16,
17, 12, 4, 15, 26,
14, 22, 9, 29, 20,
6, 2, 18, 25, 5,
1, 3, 24, 11, ]
labels = ['Computational Linguistics', 'Speech Synthesis', 'IRR', 'Neural Network', 'IRR', 'Computer Vision',
'Parallel Programming', 'Reasoning (?)','Bioinformatics','Machine Translation','Algorithms',
'Social Media', '??', 'Compiler', 'Wireless Communication', 'Machine Learning',
'Computer Simulation','Robot Control', 'Computer System', '??', 'Quantum Computing',
'Computer Security', 'Database', 'Human Computer Interaction', 'Interaction System (?)', 'Modelling (?)',
'Information Retrieval', 'Graph Theory', '??', '??']
TOPIC_DICT = dict((a,b) for a,b in zip(topics,labels))
In [77]:
f = plt.figure(figsize=(19, 10))
ax = f.add_subplot(111)
nx.draw(
g_topicnet6yr,
pos=_pos,
ax=ax,
node_size=50,
alpha=.9,
node_color=artist.color_by_inst(g_topicnet6yr, get_lookup_poinf()))
for _, node_dict in list(partition_dict.items()):
# calculate average position:
pos_x, pos_y = 0, 0
for node in node_dict['nodes']:
pos_x += _pos[node][0]
pos_y += _pos[node][1]
pos_x /= len(node_dict['nodes'])
pos_y /= len(node_dict['nodes'])
topic_num = node_dict['most_probable_topic']
ax.text(
pos_x - .15,
pos_y - .1,
"{}-{}".format(topic_num + 1, TOPIC_DICT[topic_num]),
withdash=True,
size=17,
weight='bold')
# print((node_dict['most_probable_topic']))
artist.add_inst_labels(ax)
ax.legend(
loc='upper center',
bbox_to_anchor=(0.5, 1.1),
ncol=3,
title='institutes',
fontsize=12,
fancybox=True,
shadow=False)
f.tight_layout()
In [35]:
f.savefig('IMG/topicnet6yr_sping_w_topiclabel.pdf',format='pdf',bbox_inches='tight')
In [78]:
print(nx.average_clustering(g_topicnet6yr,count_zeros=False))
# clustering coefficient
c,avg = clustering_coeff(g_topicnet6yr)
print(avg)
0.7846849412269972 0.5523768994163731
In [79]:
nx.transitivity(g_topicnet6yr)
Out[79]:
0.8257756563245824
In [80]:
a = nx.degree_histogram(g_topicnet6yr)
print(a[0]+a[1])
39
In [81]:
s=0
for i in nx.connected_components(g_topicnet6yr):
s+=1
print(s)
25
Using spectral clustering¶
In [ ]:
from sklearn.cluster import SpectralClustering
In [ ]:
spectral_c = SpectralClustering(n_clusters=24) # <- number of dimension of projection subspace
In [ ]:
topicnet['list_tm'] = topicnet.topic_probs.apply(lambda x: to_list(x,30))
In [ ]:
_x = topicnet.list_tm.tolist()
In [ ]:
spectral_clusters = spectral_c.fit_predict(X=_x)
In [ ]:
partition = {}
for i, n in enumerate(topicnet.id.tolist()):
partition[n] = spectral_clusters[i]+1
In [ ]:
# create nx graph
lookup_pub = get_lookup_pub(2012,2017)
g_infnet6yr = nx.Graph()
# add edges:
poinf_list = get_lookup_poinf().index.tolist()
edges = lookup_pub.edges.tolist()
for e in edges:
for (a, b) in e:
if a in poinf_list and b in poinf_list:
g_infnet6yr.add_edge(a, b)
In [ ]:
print(nx.info(g_infnet6yr))
In [ ]:
g_topicnet6yr_spectral = create_community_graph(partition, g_infnet6yr)
In [ ]:
_nodelist = [a for a in g_topicnet6yr_spectral if a in g_topicnet6yr]
In [ ]:
pos_infnet6yr = nx.spring_layout(g_infnet6yr)
pos_infnet6yr2 = nx.spring_layout(g_infnet6yr, pos=pos_infnet6yr)
In [ ]:
nx.draw(
g_infnet6yr,
pos=pos_infnet6yr2,
node_size=30,
node_color=artist.color_by_inst(g_infnet6yr, get_lookup_poinf()));
In [ ]:
nx.draw(
g_topicnet6yr,
pos=pos_infnet6yr2,
node_size=30,
node_color=artist.color_by_inst(g_topicnet6yr, get_lookup_poinf()));
In [ ]:
nx.draw(
g_topicnet6yr_spectral,
# pos=pos_infnet6yr2,
node_size=30,
node_color=artist.color_by_inst(g_topicnet6yr_spectral, get_lookup_poinf()));
Deriving topicnetref-6yr¶
In [82]:
# Since we are using the same base, we do not need to reload it again!
# Loading the new topic model:
tm = models.LdaModel.load('../../topicModel/src/tmdblp/tm_dblp')
dict_tm = Dictionary.load(
'../../data/data_dblp/corpora/dictionary.dblp.1997-2017')
2018-04-11 22:41:45,593 : INFO : loading LdaModel object from ../../topicModel/src/tmdblp/tm_dblp 2018-04-11 22:41:45,600 : INFO : loading expElogbeta from ../../topicModel/src/tmdblp/tm_dblp.expElogbeta.npy with mmap=None 2018-04-11 22:41:45,633 : INFO : setting ignored attribute id2word to None 2018-04-11 22:41:45,634 : INFO : setting ignored attribute dispatcher to None 2018-04-11 22:41:45,634 : INFO : setting ignored attribute state to None 2018-04-11 22:41:45,635 : INFO : loaded ../../topicModel/src/tmdblp/tm_dblp 2018-04-11 22:41:45,635 : INFO : loading LdaModel object from ../../topicModel/src/tmdblp/tm_dblp.state 2018-04-11 22:41:46,062 : INFO : loaded ../../topicModel/src/tmdblp/tm_dblp.state 2018-04-11 22:41:46,082 : INFO : loading Dictionary object from ../../data/data_dblp/corpora/dictionary.dblp.1997-2017 2018-04-11 22:41:46,125 : INFO : loaded ../../data/data_dblp/corpora/dictionary.dblp.1997-2017
In [83]:
# Generate topic probabilities for each publication using the dictionary for DBLP
tm.minimum_probability = 0.001
topicnet['topic_probs'] = topicnet.toks.apply(
lambda a: tm.get_document_topics(dict_tm.doc2bow(a)) if len(a) else None)
In [100]:
# start from 0 to get all the probs
tm.minimum_probability = 0.00
topicnet['topic_probs'] = topicnet.toks.apply(
lambda a: tm.get_document_topics(dict_tm.doc2bow(a)))
ls = ['-', '-.', '--', ':']
# ds = [(5,1),(5,5), (5,10), (5,20)]
marker = ['x', '^', 'd', 'p']
# try values for min probability:
f = plt.figure(figsize=(15, 5))
axes = []
xs = np.linspace(0, tm.num_topics, num=tm.num_topics)
# _xs = np.linspace(xs[0],xs[-1],300)
c = sns.color_palette(n_colors=3)
for i in range(3):
if i == 0:
ax = f.add_subplot(1, 3, i + 1)
else:
ax = f.add_subplot(1, 3, i + 1, sharey=ax)
_probs = np.asarray([a[1] for a in topicnet.topic_probs.iloc[13 + i * 2]])
for j, p in enumerate([0.001, 0.01, 0.1]):
ax.hlines(
p,
xs[0],
xs[-1],
linestyles=':',
linewidth=4,
label='{}'.format(p),
colors=c[j])
# _probs[_probs <= p] = 0
# ax.step(xs, _probs, alpha=0.7, linestyle='-', linewidth=4)
ax.vlines(xs,[0],_probs,alpha=0.7 )
# ax.scatter(xs,_probs,,alpha=0.6,s=50,marker=marker[j])
ax.set_yscale('log')
axes.append(ax)
ax.legend().set_visible(False)
h, l = axes[-1].get_legend_handles_labels()
axes[-1].legend(h, l, loc=0)
f.tight_layout()
In [101]:
f.savefig('IMG/try_min_probs_refdblp.png',format='png',bbox_inches='tight')
In [84]:
# Generate cosine similarity
cosim = compare_researchers(topicnet.topic_probs.tolist(), tm.num_topics)
In [85]:
# find best threshold
thresholds, edges, distances, closest_edges, best_threshold, lowest_avg_distances, best_j_dist_epoch, best_j_dist_threshold = \
find_best_threshold(adj_mat, cosim, num_iter=10000, step_size=0.001)
2018-04-11 22:42:00,560 : INFO : Number of ground_truth_edges: 361.0 2018-04-11 22:42:00,561 : INFO : binary edges: True 2018-04-11 22:42:00,571 : INFO : epoch 0: threshold: 0.001 avg_dist: 0.979 num_edges: 16799 2018-04-11 22:42:00,583 : INFO : epoch 1: threshold: 0.002 avg_dist: 0.979 num_edges: 16784 2018-04-11 22:42:00,594 : INFO : epoch 2: threshold: 0.003 avg_dist: 0.979 num_edges: 16770 2018-04-11 22:42:00,606 : INFO : epoch 3: threshold: 0.004 avg_dist: 0.978 num_edges: 16744 2018-04-11 22:42:00,618 : INFO : epoch 4: threshold: 0.005 avg_dist: 0.978 num_edges: 16710 2018-04-11 22:42:00,630 : INFO : epoch 5: threshold: 0.006 avg_dist: 0.978 num_edges: 16684 2018-04-11 22:42:00,640 : INFO : epoch 6: threshold: 0.007 avg_dist: 0.978 num_edges: 16653 2018-04-11 22:42:00,648 : INFO : epoch 7: threshold: 0.008 avg_dist: 0.978 num_edges: 16609 2018-04-11 22:42:00,656 : INFO : epoch 8: threshold: 0.009 avg_dist: 0.978 num_edges: 16557 2018-04-11 22:42:00,664 : INFO : epoch 9: threshold: 0.010 avg_dist: 0.978 num_edges: 16513 2018-04-11 22:42:00,676 : INFO : epoch 10: threshold: 0.011 avg_dist: 0.978 num_edges: 16465 2018-04-11 22:42:00,692 : INFO : epoch 11: threshold: 0.012 avg_dist: 0.978 num_edges: 16415 2018-04-11 22:42:00,702 : INFO : epoch 12: threshold: 0.013 avg_dist: 0.978 num_edges: 16351 2018-04-11 22:42:00,711 : INFO : epoch 13: threshold: 0.014 avg_dist: 0.978 num_edges: 16282 2018-04-11 22:42:00,718 : INFO : epoch 14: threshold: 0.015 avg_dist: 0.978 num_edges: 16236 2018-04-11 22:42:00,727 : INFO : epoch 15: threshold: 0.016 avg_dist: 0.978 num_edges: 16168 2018-04-11 22:42:00,735 : INFO : epoch 16: threshold: 0.017 avg_dist: 0.978 num_edges: 16113 2018-04-11 22:42:00,744 : INFO : epoch 17: threshold: 0.018 avg_dist: 0.978 num_edges: 16042 2018-04-11 22:42:00,753 : INFO : epoch 18: threshold: 0.019 avg_dist: 0.978 num_edges: 15988 2018-04-11 22:42:00,767 : INFO : epoch 19: threshold: 0.020 avg_dist: 0.977 num_edges: 15923 2018-04-11 22:42:00,776 : INFO : epoch 20: threshold: 0.021 avg_dist: 0.977 num_edges: 15855 2018-04-11 22:42:00,787 : INFO : epoch 21: threshold: 0.022 avg_dist: 0.977 num_edges: 15770 2018-04-11 22:42:00,794 : INFO : epoch 22: threshold: 0.023 avg_dist: 0.977 num_edges: 15687 2018-04-11 22:42:00,803 : INFO : epoch 23: threshold: 0.024 avg_dist: 0.977 num_edges: 15616 2018-04-11 22:42:00,810 : INFO : epoch 24: threshold: 0.025 avg_dist: 0.977 num_edges: 15522 2018-04-11 22:42:00,820 : INFO : epoch 25: threshold: 0.026 avg_dist: 0.977 num_edges: 15438 2018-04-11 22:42:00,833 : INFO : epoch 26: threshold: 0.027 avg_dist: 0.976 num_edges: 15359 2018-04-11 22:42:00,844 : INFO : epoch 27: threshold: 0.028 avg_dist: 0.976 num_edges: 15276 2018-04-11 22:42:00,852 : INFO : epoch 28: threshold: 0.029 avg_dist: 0.976 num_edges: 15190 2018-04-11 22:42:00,861 : INFO : epoch 29: threshold: 0.030 avg_dist: 0.976 num_edges: 15110 2018-04-11 22:42:00,873 : INFO : epoch 30: threshold: 0.031 avg_dist: 0.976 num_edges: 15033 2018-04-11 22:42:00,888 : INFO : epoch 31: threshold: 0.032 avg_dist: 0.976 num_edges: 14927 2018-04-11 22:42:00,901 : INFO : epoch 32: threshold: 0.033 avg_dist: 0.976 num_edges: 14830 2018-04-11 22:42:00,911 : INFO : epoch 33: threshold: 0.034 avg_dist: 0.975 num_edges: 14750 2018-04-11 22:42:00,923 : INFO : epoch 34: threshold: 0.035 avg_dist: 0.975 num_edges: 14661 2018-04-11 22:42:00,936 : INFO : epoch 35: threshold: 0.036 avg_dist: 0.975 num_edges: 14594 2018-04-11 22:42:00,943 : INFO : epoch 36: threshold: 0.037 avg_dist: 0.975 num_edges: 14510 2018-04-11 22:42:00,951 : INFO : epoch 37: threshold: 0.038 avg_dist: 0.975 num_edges: 14439 2018-04-11 22:42:00,964 : INFO : epoch 38: threshold: 0.039 avg_dist: 0.975 num_edges: 14357 2018-04-11 22:42:00,976 : INFO : epoch 39: threshold: 0.040 avg_dist: 0.974 num_edges: 14266 2018-04-11 22:42:00,984 : INFO : epoch 40: threshold: 0.041 avg_dist: 0.974 num_edges: 14166 2018-04-11 22:42:00,994 : INFO : epoch 41: threshold: 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epoch 600: threshold: 0.601 avg_dist: 0.796 num_edges: 1247 2018-04-11 22:42:05,753 : INFO : epoch 601: threshold: 0.602 avg_dist: 0.796 num_edges: 1241 2018-04-11 22:42:05,761 : INFO : epoch 602: threshold: 0.603 avg_dist: 0.795 num_edges: 1236 2018-04-11 22:42:05,767 : INFO : epoch 603: threshold: 0.604 avg_dist: 0.796 num_edges: 1233 2018-04-11 22:42:05,777 : INFO : epoch 604: threshold: 0.605 avg_dist: 0.796 num_edges: 1231 2018-04-11 22:42:05,784 : INFO : epoch 605: threshold: 0.606 avg_dist: 0.795 num_edges: 1227 2018-04-11 22:42:05,792 : INFO : epoch 606: threshold: 0.607 avg_dist: 0.795 num_edges: 1224 2018-04-11 22:42:05,803 : INFO : epoch 607: threshold: 0.608 avg_dist: 0.794 num_edges: 1221 2018-04-11 22:42:05,813 : INFO : epoch 608: threshold: 0.609 avg_dist: 0.794 num_edges: 1216 2018-04-11 22:42:05,821 : INFO : epoch 609: threshold: 0.610 avg_dist: 0.793 num_edges: 1214 2018-04-11 22:42:05,828 : INFO : epoch 610: threshold: 0.611 avg_dist: 0.793 num_edges: 1210 2018-04-11 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num_edges: 1154 2018-04-11 22:42:05,927 : INFO : epoch 622: threshold: 0.623 avg_dist: 0.789 num_edges: 1152 2018-04-11 22:42:05,934 : INFO : epoch 623: threshold: 0.624 avg_dist: 0.790 num_edges: 1144 2018-04-11 22:42:05,946 : INFO : epoch 624: threshold: 0.625 avg_dist: 0.789 num_edges: 1141 2018-04-11 22:42:05,954 : INFO : epoch 625: threshold: 0.626 avg_dist: 0.789 num_edges: 1136 2018-04-11 22:42:05,963 : INFO : epoch 626: threshold: 0.627 avg_dist: 0.789 num_edges: 1130 2018-04-11 22:42:05,969 : INFO : epoch 627: threshold: 0.628 avg_dist: 0.788 num_edges: 1123 2018-04-11 22:42:05,978 : INFO : epoch 628: threshold: 0.629 avg_dist: 0.788 num_edges: 1118 2018-04-11 22:42:05,986 : INFO : epoch 629: threshold: 0.630 avg_dist: 0.787 num_edges: 1116 2018-04-11 22:42:05,997 : INFO : epoch 630: threshold: 0.631 avg_dist: 0.787 num_edges: 1112 2018-04-11 22:42:06,004 : INFO : epoch 631: threshold: 0.632 avg_dist: 0.787 num_edges: 1111 2018-04-11 22:42:06,011 : INFO : epoch 632: threshold: 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epoch 643: threshold: 0.644 avg_dist: 0.784 num_edges: 1057 2018-04-11 22:42:06,105 : INFO : epoch 644: threshold: 0.645 avg_dist: 0.784 num_edges: 1054 2018-04-11 22:42:06,114 : INFO : epoch 645: threshold: 0.646 avg_dist: 0.784 num_edges: 1051 2018-04-11 22:42:06,122 : INFO : epoch 646: threshold: 0.647 avg_dist: 0.783 num_edges: 1047 2018-04-11 22:42:06,130 : INFO : epoch 647: threshold: 0.648 avg_dist: 0.782 num_edges: 1041 2018-04-11 22:42:06,138 : INFO : epoch 648: threshold: 0.649 avg_dist: 0.781 num_edges: 1038 2018-04-11 22:42:06,145 : INFO : epoch 649: threshold: 0.650 avg_dist: 0.781 num_edges: 1037 2018-04-11 22:42:06,152 : INFO : epoch 650: threshold: 0.651 avg_dist: 0.781 num_edges: 1035 2018-04-11 22:42:06,160 : INFO : epoch 651: threshold: 0.652 avg_dist: 0.780 num_edges: 1032 2018-04-11 22:42:06,167 : INFO : epoch 652: threshold: 0.653 avg_dist: 0.779 num_edges: 1028 2018-04-11 22:42:06,174 : INFO : epoch 653: threshold: 0.654 avg_dist: 0.778 num_edges: 1026 2018-04-11 22:42:06,183 : INFO : epoch 654: threshold: 0.655 avg_dist: 0.778 num_edges: 1023 2018-04-11 22:42:06,191 : INFO : epoch 655: threshold: 0.656 avg_dist: 0.780 num_edges: 1017 2018-04-11 22:42:06,198 : INFO : epoch 656: threshold: 0.657 avg_dist: 0.776 num_edges: 1012 2018-04-11 22:42:06,206 : INFO : epoch 657: threshold: 0.658 avg_dist: 0.775 num_edges: 1011 2018-04-11 22:42:06,215 : INFO : epoch 658: threshold: 0.659 avg_dist: 0.774 num_edges: 1007 2018-04-11 22:42:06,222 : INFO : epoch 659: threshold: 0.660 avg_dist: 0.774 num_edges: 1005 2018-04-11 22:42:06,234 : INFO : epoch 660: threshold: 0.661 avg_dist: 0.775 num_edges: 1002 2018-04-11 22:42:06,242 : INFO : epoch 661: threshold: 0.662 avg_dist: 0.774 num_edges: 1000 2018-04-11 22:42:06,251 : INFO : epoch 662: threshold: 0.663 avg_dist: 0.774 num_edges: 995 2018-04-11 22:42:06,260 : INFO : epoch 663: threshold: 0.664 avg_dist: 0.773 num_edges: 991 2018-04-11 22:42:06,271 : INFO : epoch 664: threshold: 0.665 avg_dist: 0.772 num_edges: 983 2018-04-11 22:42:06,279 : INFO : epoch 665: threshold: 0.666 avg_dist: 0.773 num_edges: 979 2018-04-11 22:42:06,286 : INFO : epoch 666: threshold: 0.667 avg_dist: 0.773 num_edges: 977 2018-04-11 22:42:06,298 : INFO : epoch 667: threshold: 0.668 avg_dist: 0.771 num_edges: 974 2018-04-11 22:42:06,305 : INFO : epoch 668: threshold: 0.669 avg_dist: 0.771 num_edges: 971 2018-04-11 22:42:06,313 : INFO : epoch 669: threshold: 0.670 avg_dist: 0.770 num_edges: 967 2018-04-11 22:42:06,320 : INFO : epoch 670: threshold: 0.671 avg_dist: 0.771 num_edges: 962 2018-04-11 22:42:06,328 : INFO : epoch 671: threshold: 0.672 avg_dist: 0.771 num_edges: 961 2018-04-11 22:42:06,336 : INFO : epoch 672: threshold: 0.673 avg_dist: 0.769 num_edges: 958 2018-04-11 22:42:06,344 : INFO : epoch 673: threshold: 0.674 avg_dist: 0.773 num_edges: 948 2018-04-11 22:42:06,350 : INFO : epoch 674: threshold: 0.675 avg_dist: 0.772 num_edges: 944 2018-04-11 22:42:06,358 : INFO : epoch 675: threshold: 0.676 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epoch 697: threshold: 0.698 avg_dist: 0.778 num_edges: 837 2018-04-11 22:42:06,549 : INFO : epoch 698: threshold: 0.699 avg_dist: 0.777 num_edges: 834 2018-04-11 22:42:06,557 : INFO : epoch 699: threshold: 0.700 avg_dist: 0.777 num_edges: 829 2018-04-11 22:42:06,563 : INFO : epoch 700: threshold: 0.701 avg_dist: 0.777 num_edges: 827 2018-04-11 22:42:06,570 : INFO : epoch 701: threshold: 0.702 avg_dist: 0.779 num_edges: 822 2018-04-11 22:42:06,580 : INFO : epoch 702: threshold: 0.703 avg_dist: 0.779 num_edges: 821 2018-04-11 22:42:06,587 : INFO : epoch 703: threshold: 0.704 avg_dist: 0.782 num_edges: 817 2018-04-11 22:42:06,595 : INFO : epoch 704: threshold: 0.705 avg_dist: 0.781 num_edges: 815 2018-04-11 22:42:06,605 : INFO : epoch 705: threshold: 0.706 avg_dist: 0.780 num_edges: 813 2018-04-11 22:42:06,611 : INFO : epoch 706: threshold: 0.707 avg_dist: 0.780 num_edges: 812 2018-04-11 22:42:06,619 : INFO : epoch 707: threshold: 0.708 avg_dist: 0.781 num_edges: 807 2018-04-11 22:42:06,630 : INFO : epoch 708: threshold: 0.709 avg_dist: 0.782 num_edges: 804 2018-04-11 22:42:06,638 : INFO : epoch 709: threshold: 0.710 avg_dist: 0.785 num_edges: 800 2018-04-11 22:42:06,646 : INFO : epoch 710: threshold: 0.711 avg_dist: 0.788 num_edges: 796 2018-04-11 22:42:06,655 : INFO : epoch 711: threshold: 0.712 avg_dist: 0.788 num_edges: 790 2018-04-11 22:42:06,663 : INFO : epoch 712: threshold: 0.713 avg_dist: 0.789 num_edges: 788 2018-04-11 22:42:06,670 : INFO : epoch 713: threshold: 0.714 avg_dist: 0.788 num_edges: 787 2018-04-11 22:42:06,679 : INFO : epoch 714: threshold: 0.715 avg_dist: 0.785 num_edges: 782 2018-04-11 22:42:06,685 : INFO : epoch 715: threshold: 0.716 avg_dist: 0.783 num_edges: 778 2018-04-11 22:42:06,693 : INFO : epoch 716: threshold: 0.717 avg_dist: 0.780 num_edges: 773 2018-04-11 22:42:06,701 : INFO : epoch 717: threshold: 0.718 avg_dist: 0.781 num_edges: 771 2018-04-11 22:42:06,709 : INFO : epoch 718: threshold: 0.719 avg_dist: 0.783 num_edges: 765 2018-04-11 22:42:06,716 : INFO : epoch 719: threshold: 0.720 avg_dist: 0.783 num_edges: 764 2018-04-11 22:42:06,723 : INFO : epoch 720: threshold: 0.721 avg_dist: 0.783 num_edges: 762 2018-04-11 22:42:06,729 : INFO : epoch 721: threshold: 0.722 avg_dist: 0.782 num_edges: 757 2018-04-11 22:42:06,736 : INFO : epoch 722: threshold: 0.723 avg_dist: 0.787 num_edges: 752 2018-04-11 22:42:06,744 : INFO : epoch 723: threshold: 0.724 avg_dist: 0.786 num_edges: 747 2018-04-11 22:42:06,751 : INFO : epoch 724: threshold: 0.725 avg_dist: 0.786 num_edges: 744 2018-04-11 22:42:06,758 : INFO : epoch 725: threshold: 0.726 avg_dist: 0.783 num_edges: 740 2018-04-11 22:42:06,765 : INFO : epoch 726: threshold: 0.727 avg_dist: 0.782 num_edges: 737 2018-04-11 22:42:06,773 : INFO : epoch 727: threshold: 0.728 avg_dist: 0.786 num_edges: 733 2018-04-11 22:42:06,784 : INFO : epoch 728: threshold: 0.729 avg_dist: 0.786 num_edges: 727 2018-04-11 22:42:06,794 : INFO : epoch 729: threshold: 0.730 avg_dist: 0.786 num_edges: 720 2018-04-11 22:42:06,801 : INFO : epoch 730: threshold: 0.731 avg_dist: 0.786 num_edges: 716 2018-04-11 22:42:06,808 : INFO : epoch 731: threshold: 0.732 avg_dist: 0.787 num_edges: 709 2018-04-11 22:42:06,815 : INFO : epoch 732: threshold: 0.733 avg_dist: 0.786 num_edges: 703 2018-04-11 22:42:06,822 : INFO : epoch 733: threshold: 0.734 avg_dist: 0.786 num_edges: 702 2018-04-11 22:42:06,835 : INFO : epoch 734: threshold: 0.735 avg_dist: 0.787 num_edges: 697 2018-04-11 22:42:06,842 : INFO : epoch 735: threshold: 0.736 avg_dist: 0.787 num_edges: 690 2018-04-11 22:42:06,851 : INFO : epoch 736: threshold: 0.737 avg_dist: 0.788 num_edges: 683 2018-04-11 22:42:06,858 : INFO : epoch 737: threshold: 0.738 avg_dist: 0.788 num_edges: 680 2018-04-11 22:42:06,866 : INFO : epoch 738: threshold: 0.739 avg_dist: 0.787 num_edges: 675 2018-04-11 22:42:06,876 : INFO : epoch 739: threshold: 0.740 avg_dist: 0.786 num_edges: 671 2018-04-11 22:42:06,887 : INFO : epoch 740: threshold: 0.741 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epoch 762: threshold: 0.763 avg_dist: 0.783 num_edges: 585 2018-04-11 22:42:07,059 : INFO : epoch 763: threshold: 0.764 avg_dist: 0.783 num_edges: 583 2018-04-11 22:42:07,074 : INFO : epoch 764: threshold: 0.765 avg_dist: 0.784 num_edges: 580 2018-04-11 22:42:07,086 : INFO : epoch 765: threshold: 0.766 avg_dist: 0.783 num_edges: 576 2018-04-11 22:42:07,093 : INFO : epoch 766: threshold: 0.767 avg_dist: 0.785 num_edges: 573 2018-04-11 22:42:07,100 : INFO : epoch 767: threshold: 0.768 avg_dist: 0.786 num_edges: 570 2018-04-11 22:42:07,111 : INFO : epoch 768: threshold: 0.769 avg_dist: 0.784 num_edges: 565 2018-04-11 22:42:07,118 : INFO : epoch 769: threshold: 0.770 avg_dist: 0.785 num_edges: 562 2018-04-11 22:42:07,125 : INFO : epoch 770: threshold: 0.771 avg_dist: 0.784 num_edges: 558 2018-04-11 22:42:07,133 : INFO : epoch 771: threshold: 0.772 avg_dist: 0.784 num_edges: 556 2018-04-11 22:42:07,140 : INFO : epoch 772: threshold: 0.773 avg_dist: 0.788 num_edges: 551 2018-04-11 22:42:07,152 : INFO : epoch 773: threshold: 0.774 avg_dist: 0.788 num_edges: 550 2018-04-11 22:42:07,161 : INFO : epoch 774: threshold: 0.775 avg_dist: 0.788 num_edges: 549 2018-04-11 22:42:07,171 : INFO : epoch 775: threshold: 0.776 avg_dist: 0.787 num_edges: 546 2018-04-11 22:42:07,178 : INFO : epoch 776: threshold: 0.777 avg_dist: 0.786 num_edges: 543 2018-04-11 22:42:07,184 : INFO : epoch 777: threshold: 0.778 avg_dist: 0.789 num_edges: 537 2018-04-11 22:42:07,191 : INFO : epoch 778: threshold: 0.779 avg_dist: 0.789 num_edges: 534 2018-04-11 22:42:07,199 : INFO : epoch 779: threshold: 0.780 avg_dist: 0.788 num_edges: 526 2018-04-11 22:42:07,205 : INFO : epoch 780: threshold: 0.781 avg_dist: 0.789 num_edges: 521 2018-04-11 22:42:07,214 : INFO : epoch 781: threshold: 0.782 avg_dist: 0.788 num_edges: 518 2018-04-11 22:42:07,220 : INFO : epoch 782: threshold: 0.783 avg_dist: 0.788 num_edges: 517 2018-04-11 22:42:07,227 : INFO : epoch 783: threshold: 0.784 avg_dist: 0.789 num_edges: 513 2018-04-11 22:42:07,234 : INFO : epoch 784: threshold: 0.785 avg_dist: 0.788 num_edges: 509 2018-04-11 22:42:07,242 : INFO : epoch 785: threshold: 0.786 avg_dist: 0.790 num_edges: 502 2018-04-11 22:42:07,253 : INFO : epoch 786: threshold: 0.787 avg_dist: 0.796 num_edges: 498 2018-04-11 22:42:07,260 : INFO : epoch 787: threshold: 0.788 avg_dist: 0.798 num_edges: 492 2018-04-11 22:42:07,267 : INFO : epoch 788: threshold: 0.789 avg_dist: 0.790 num_edges: 487 2018-04-11 22:42:07,273 : INFO : epoch 789: threshold: 0.790 avg_dist: 0.786 num_edges: 484 2018-04-11 22:42:07,280 : INFO : epoch 790: threshold: 0.791 avg_dist: 0.794 num_edges: 481 2018-04-11 22:42:07,288 : INFO : epoch 791: threshold: 0.792 avg_dist: 0.794 num_edges: 479 2018-04-11 22:42:07,296 : INFO : epoch 792: threshold: 0.793 avg_dist: 0.794 num_edges: 474 2018-04-11 22:42:07,307 : INFO : epoch 793: threshold: 0.794 avg_dist: 0.793 num_edges: 472 2018-04-11 22:42:07,314 : INFO : epoch 794: threshold: 0.795 avg_dist: 0.793 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22:42:07,659 : INFO : epoch 838: threshold: 0.839 avg_dist: 0.815 num_edges: 332 2018-04-11 22:42:07,668 : INFO : epoch 839: threshold: 0.840 avg_dist: 0.815 num_edges: 332 2018-04-11 22:42:07,679 : INFO : epoch 840: threshold: 0.841 avg_dist: 0.814 num_edges: 329 2018-04-11 22:42:07,688 : INFO : epoch 841: threshold: 0.842 avg_dist: 0.814 num_edges: 326 2018-04-11 22:42:07,696 : INFO : epoch 842: threshold: 0.843 avg_dist: 0.815 num_edges: 321 2018-04-11 22:42:07,704 : INFO : epoch 843: threshold: 0.844 avg_dist: 0.815 num_edges: 320 2018-04-11 22:42:07,711 : INFO : epoch 844: threshold: 0.845 avg_dist: 0.815 num_edges: 316 2018-04-11 22:42:07,717 : INFO : epoch 845: threshold: 0.846 avg_dist: 0.815 num_edges: 309 2018-04-11 22:42:07,727 : INFO : epoch 846: threshold: 0.847 avg_dist: 0.815 num_edges: 307 2018-04-11 22:42:07,734 : INFO : epoch 847: threshold: 0.848 avg_dist: 0.816 num_edges: 303 2018-04-11 22:42:07,742 : INFO : epoch 848: threshold: 0.849 avg_dist: 0.815 num_edges: 301 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0.980 avg_dist: 0.968 num_edges: 17 2018-04-11 22:42:08,894 : INFO : epoch 980: threshold: 0.981 avg_dist: 0.968 num_edges: 17 2018-04-11 22:42:08,904 : INFO : epoch 981: threshold: 0.982 avg_dist: 0.970 num_edges: 16 2018-04-11 22:42:08,916 : INFO : epoch 982: threshold: 0.983 avg_dist: 0.976 num_edges: 15 2018-04-11 22:42:08,928 : INFO : epoch 983: threshold: 0.984 avg_dist: 0.981 num_edges: 12 2018-04-11 22:42:08,935 : INFO : epoch 984: threshold: 0.985 avg_dist: 0.981 num_edges: 12 2018-04-11 22:42:08,943 : INFO : epoch 985: threshold: 0.986 avg_dist: 0.981 num_edges: 12 2018-04-11 22:42:08,954 : INFO : epoch 986: threshold: 0.987 avg_dist: 0.982 num_edges: 10 2018-04-11 22:42:08,967 : INFO : epoch 987: threshold: 0.988 avg_dist: 0.986 num_edges: 8 2018-04-11 22:42:08,980 : INFO : epoch 988: threshold: 0.989 avg_dist: 0.988 num_edges: 7 2018-04-11 22:42:08,992 : INFO : epoch 989: threshold: 0.990 avg_dist: 0.990 num_edges: 5 2018-04-11 22:42:09,001 : INFO : epoch 990: threshold: 0.991 avg_dist: 0.990 num_edges: 5 2018-04-11 22:42:09,013 : INFO : epoch 991: threshold: 0.992 avg_dist: 0.990 num_edges: 5 2018-04-11 22:42:09,020 : INFO : epoch 992: threshold: 0.993 avg_dist: 0.991 num_edges: 4 2018-04-11 22:42:09,028 : INFO : epoch 993: threshold: 0.994 avg_dist: 0.992 num_edges: 3 2018-04-11 22:42:09,035 : INFO : epoch 994: threshold: 0.995 avg_dist: 0.995 num_edges: 1 2018-04-11 22:42:09,042 : INFO : epoch 995: threshold: 0.996 avg_dist: 0.995 num_edges: 1 2018-04-11 22:42:09,050 : INFO : epoch 996: threshold: 0.997 avg_dist: 0.995 num_edges: 1 2018-04-11 22:42:09,057 : INFO : epoch 997: threshold: 0.998 avg_dist: 0.995 num_edges: 1 2018-04-11 22:42:09,064 : INFO : epoch 998: threshold: 0.999 avg_dist: 0.995 num_edges: 1
threshold: 0.832 dist 0.769098 (675, 0.676) edges 361/361.0 (831)
threshold: 0.832 dist 0.767666 (672, 0.673) edges 361/360.0 (831)
In [86]:
# Set the edges:
topicnet_adj_mat = set_edges(cosim, 0.832, binary=True)
topicnet_adj_mat.dump('./res/topicnetref6yr_adjmat.pkl')
2018-04-11 22:42:10,149 : INFO : dimension: 184, 184
Visualise¶
Thresholding:¶
In [87]:
f = threshold_plot(thresholds, distances, edges, best_threshold, closest_edges,
best_j_dist_threshold, lowest_avg_distances,
np.sum(adj_mat) // 2, cosim)
In [ ]:
f.savefig(
'./IMG/topicnetref6yr_thresholding.png', format='png', bbox_inches='tight')
As series of matrices:¶
In [88]:
# ORIGINAL COLALB NET
f1 = plt.figure(figsize=(8, 8))
ax1 = f1.add_subplot(111)
ax1.axis('off')
sns.heatmap(adj_mat, cbar=False, square=True)
# ax1.set_title('infnet6yr\n(#Edges={})'.format(int(np.sum(adj_mat) // 2)))
# COSIM
f2 = plt.figure(figsize=(8, 8))
ax2 = f2.add_subplot(111)
ax2.axis('off')
sns.heatmap(
cosim,
cbar=False,
square=True,
ax=ax2,
vmax=1,
vmin=0,
robust=True,
cmap='gnuplot2')
# ax2.set_title('Cosine similairty\nbetween researchers')
# FINAL :
f3 = plt.figure(figsize=(8, 8))
ax3 = f3.add_subplot(111)
ax3.axis('off')
sns.heatmap(topicnet_adj_mat, cbar=False, square=True, ax=ax3)
# ax3.set_title('topicnet6yr\n($\epsilon$={:.3f}, #Edges={})'.format(
# best_threshold,
# int(np.sum(topicnet_adj_mat) // 2)))
Out[88]:
<matplotlib.axes._subplots.AxesSubplot at 0x7fd03ef81e48>
In [155]:
f3.savefig('./IMG/topicnetref6yr_adjmats_topicnet.png')
As networks:¶
In [89]:
g_topicnetref6yr = adj_mat_to_graph(topicnet_adj_mat, collabnet.id.tolist())
In [90]:
print(nx.info(g_topicnetref6yr))
Name: Type: Graph Number of nodes: 119 Number of edges: 361 Average degree: 6.0672
In [91]:
artist.draw_default_layout(
g_topicnetref6yr,
lookup_poinf=get_lookup_poinf(),
file_prefix='topicnetref6yr',
SAVE_GRAPHS=True)
2018-04-11 22:42:25,071 : INFO : SAVE_GRAPHS: True
In [92]:
artist.draw_circular_layout(
g_topicnetref6yr,
get_lookup_poinf(),
file_prefix='topicnetref6yr',
SAVE_GRAPHS=True)
2018-04-11 22:42:28,733 : INFO : SAVE_GRAPHS: True
In [93]:
pos = nx.spring_layout(g_topicnetref6yr,iterations=70, scale=3)
In [97]:
# Visualise with topic label
partition_dict = {}
topicnet['list_tm'] = topicnet.topic_probs.apply(lambda x: to_list(x, 100))
n = 0
for i, nodes in enumerate(nx.connected_components(g_topicnetref6yr)):
n += len(nodes)
a = topicnet[topicnet.id.isin(nodes)].list_tm.tolist()
topic_dist = np.mean(a, axis=0)
partition_dict[i] = {
'nodes': nodes,
'topic_dist': topic_dist,
'most_probable_topic': np.argmax(topic_dist)
}
# dblp_ = [(40 ,'Computer Networks'),
# (80 ,'??'),
# (76 ,'Computer architecture'),
# (64 ,'Linear Algebra'),
# (96 ,'IRR'),
# (43 ,'Transportation' ),
# (69 ,'OOP'),
# (71 ,'Formal Language'),
# (95 ,'Computer System'),
# (8 ,'IOT'),
# (36 ,'Speech Processing'),
# (73 ,'Genes'),
# (15 ,'Signal Processing'),
# (6, 'Computer Networks'),
# (57 ,'??'),
# (5 ,'Machine Learning'),
# (2, 'Management'),
# (23, 'Graph Theory'),
# (17, 'Control Theory'),
# (52, 'SVM'),
# (59, 'Neural Networks'),
# (1 ,'Algorithm')]
dblp_ = [(80, 'Parallel systems'),
(76, 'Project management'),
(64, 'Bioinformatics'),
(96, 'Cryptography'),
(59, 'IOT'),
(52, '??'),
(69, 'AI'),
(17, 'Social Media'),
(95, 'NLP'),
(8, 'Education (?)'),
(40, 'Logic'),
(36, 'Web (?)'),
(71, 'Probability'),
(23, 'Neural Network'),
(5, 'Modelling'),
(2, 'Agent Based System')
]
TOPIC_DICT = dict((a,b) for a,b in dblp_)
f = plt.figure(figsize=(15, 10))
ax = f.add_subplot(111)
nx.draw(
g_topicnetref6yr,
pos=pos,
ax=ax,
node_size=50,
alpha=.9,
node_color=artist.color_by_inst(g_topicnetref6yr, get_lookup_poinf()))
for _, node_dict in list(partition_dict.items()):
# calculate average position:
pos_x, pos_y = 0, 0
for node in node_dict['nodes']:
pos_x += pos[node][0]
pos_y += pos[node][1]
pos_x /= len(node_dict['nodes'])
pos_y /= len(node_dict['nodes'])
topic_num = node_dict['most_probable_topic']
ax.text(
pos_x-.07,
pos_y + .2,
"{}-{}".format(topic_num + 1, TOPIC_DICT[topic_num]),
withdash=True,
size=17,
weight='bold')
print((node_dict['most_probable_topic']))
artist.add_inst_labels(ax)
ax.legend(
# loc=0,
loc='upper center',
bbox_to_anchor=(.6, 1.1),
ncol=3,
title='institutes',
fontsize=10,
fancybox=True,
shadow=False)
f.tight_layout()
80 76 64 96 59 69 17 95 8 40 8 36 71 23 52 96 5 2
In [ ]:
f.savefig('IMG/topicnet6yrref_w_topic.pdf',format='pdf',bbox_inches='tight')
Descriptive Statistics¶
In [98]:
gccs = generateGCC(g_topicnetref6yr)
component 1: 37.82% component 2: 13.45% component 3: 10.08% component 4: 9.24% component 5: 3.36% component 6: 2.52% component 7: 2.52% component 8: 2.52% component 9: 2.52% component 10: 2.52% component 11: 1.68% component 12: 1.68% component 13: 1.68% component 14: 1.68% component 15: 1.68% component 16: 1.68% component 17: 1.68% component 18: 1.68%
In [99]:
nx.average_clustering(g_topicnet6yr,count_zeros=False)
Out[99]:
0.7846849412269972
In [100]:
np.mean([a for a in c_coeff.values() if a>0])
Out[100]:
0.8174458366359774
In [101]:
c_coeff , avg = clustering_coeff(g_topicnetref6yr)
print(avg)
print(nx.transitivity(g_topicnetref6yr))
0.5336909101780699 0.7741935483870968
In [102]:
nx.degree_histogram(g_topicnetref6yr)
Out[102]:
[0, 32, 16, 6, 7, 7, 9, 4, 13, 4, 2, 1, 0, 1, 0, 0, 3, 1, 7, 4, 1, 0, 0, 0, 1]
Deriving topicnetref-6yr-w¶
In [103]:
# load collab net infnet6yrw
collabnet = pd.read_csv(
'../../data/data_schoolofinf/poinf_collabgraph_2012-2017_weighted.txt',sep='\n',names=['id'])
adj_mat = np.load(
'../../data/data_schoolofinf/mat/infnet6yrs-weighted-adj-mat.order6yr.pkl')
In [104]:
# nothing to work on for topicmodel since it is the same topic model...
# Calculate cosine similairty:
cosim = compare_researchers(topicnet.topic_probs.tolist(), tm.num_topics)
In [105]:
# find the best threshold:
thresholds, edges, distances, closest_edges, best_threshold, lowest_avg_distances, best_j_dist_epoch, best_j_dist_threshold = \
find_best_threshold(adj_mat, cosim, num_iter=10000, step_size=0.001,binary_edges=False)
2018-04-11 22:44:12,274 : INFO : min weight: 0.00101010101010101 2018-04-11 22:44:12,275 : INFO : Number of ground_truth_edges: 361 2018-04-11 22:44:12,275 : INFO : binary edges: False 2018-04-11 22:44:12,288 : INFO : epoch 0: threshold: 0.001 avg_dist: 0.999 num_edges: 16799 2018-04-11 22:44:12,295 : INFO : epoch 1: threshold: 0.002 avg_dist: 0.999 num_edges: 16784 2018-04-11 22:44:12,301 : INFO : epoch 2: threshold: 0.003 avg_dist: 0.999 num_edges: 16770 2018-04-11 22:44:12,308 : INFO : epoch 3: threshold: 0.004 avg_dist: 0.999 num_edges: 16744 2018-04-11 22:44:12,315 : INFO : epoch 4: threshold: 0.005 avg_dist: 0.999 num_edges: 16710 2018-04-11 22:44:12,324 : INFO : epoch 5: threshold: 0.006 avg_dist: 0.999 num_edges: 16684 2018-04-11 22:44:12,331 : INFO : epoch 6: threshold: 0.007 avg_dist: 0.999 num_edges: 16653 2018-04-11 22:44:12,344 : INFO : epoch 7: threshold: 0.008 avg_dist: 0.999 num_edges: 16609 2018-04-11 22:44:12,356 : INFO : epoch 8: threshold: 0.009 avg_dist: 0.999 num_edges: 16557 2018-04-11 22:44:12,364 : INFO : epoch 9: threshold: 0.010 avg_dist: 0.999 num_edges: 16513 2018-04-11 22:44:12,372 : INFO : epoch 10: threshold: 0.011 avg_dist: 0.999 num_edges: 16465 2018-04-11 22:44:12,380 : INFO : epoch 11: threshold: 0.012 avg_dist: 0.999 num_edges: 16415 2018-04-11 22:44:12,389 : INFO : epoch 12: threshold: 0.013 avg_dist: 0.999 num_edges: 16351 2018-04-11 22:44:12,397 : INFO : epoch 13: threshold: 0.014 avg_dist: 0.999 num_edges: 16282 2018-04-11 22:44:12,407 : INFO : epoch 14: threshold: 0.015 avg_dist: 0.999 num_edges: 16236 2018-04-11 22:44:12,415 : INFO : epoch 15: threshold: 0.016 avg_dist: 0.999 num_edges: 16168 2018-04-11 22:44:12,424 : INFO : epoch 16: threshold: 0.017 avg_dist: 0.999 num_edges: 16113 2018-04-11 22:44:12,433 : INFO : epoch 17: threshold: 0.018 avg_dist: 0.999 num_edges: 16042 2018-04-11 22:44:12,441 : INFO : epoch 18: threshold: 0.019 avg_dist: 0.999 num_edges: 15988 2018-04-11 22:44:12,449 : INFO : epoch 19: threshold: 0.020 avg_dist: 0.999 num_edges: 15923 2018-04-11 22:44:12,460 : INFO : epoch 20: threshold: 0.021 avg_dist: 0.999 num_edges: 15855 2018-04-11 22:44:12,468 : INFO : epoch 21: threshold: 0.022 avg_dist: 0.999 num_edges: 15770 2018-04-11 22:44:12,480 : INFO : epoch 22: threshold: 0.023 avg_dist: 0.999 num_edges: 15687 2018-04-11 22:44:12,493 : INFO : epoch 23: threshold: 0.024 avg_dist: 0.999 num_edges: 15616 2018-04-11 22:44:12,500 : INFO : epoch 24: threshold: 0.025 avg_dist: 0.999 num_edges: 15522 2018-04-11 22:44:12,510 : INFO : epoch 25: threshold: 0.026 avg_dist: 0.999 num_edges: 15438 2018-04-11 22:44:12,523 : INFO : epoch 26: threshold: 0.027 avg_dist: 0.999 num_edges: 15359 2018-04-11 22:44:12,531 : INFO : epoch 27: threshold: 0.028 avg_dist: 0.999 num_edges: 15276 2018-04-11 22:44:12,540 : INFO : epoch 28: threshold: 0.029 avg_dist: 0.999 num_edges: 15190 2018-04-11 22:44:12,553 : INFO : epoch 29: threshold: 0.030 avg_dist: 0.999 num_edges: 15110 2018-04-11 22:44:12,563 : INFO : epoch 30: threshold: 0.031 avg_dist: 0.999 num_edges: 15033 2018-04-11 22:44:12,577 : INFO : epoch 31: threshold: 0.032 avg_dist: 0.999 num_edges: 14927 2018-04-11 22:44:12,592 : INFO : epoch 32: threshold: 0.033 avg_dist: 0.999 num_edges: 14830 2018-04-11 22:44:12,604 : INFO : epoch 33: threshold: 0.034 avg_dist: 0.999 num_edges: 14750 2018-04-11 22:44:12,613 : INFO : epoch 34: threshold: 0.035 avg_dist: 0.999 num_edges: 14661 2018-04-11 22:44:12,621 : INFO : epoch 35: threshold: 0.036 avg_dist: 0.999 num_edges: 14594 2018-04-11 22:44:12,631 : INFO : epoch 36: threshold: 0.037 avg_dist: 0.999 num_edges: 14510 2018-04-11 22:44:12,640 : INFO : epoch 37: threshold: 0.038 avg_dist: 0.999 num_edges: 14439 2018-04-11 22:44:12,650 : INFO : epoch 38: threshold: 0.039 avg_dist: 0.999 num_edges: 14357 2018-04-11 22:44:12,658 : INFO : epoch 39: threshold: 0.040 avg_dist: 0.999 num_edges: 14266 2018-04-11 22:44:12,666 : INFO : epoch 40: threshold: 0.041 avg_dist: 0.999 num_edges: 14166 2018-04-11 22:44:12,680 : INFO : epoch 41: threshold: 0.042 avg_dist: 0.999 num_edges: 14082 2018-04-11 22:44:12,692 : INFO : epoch 42: threshold: 0.043 avg_dist: 0.999 num_edges: 14005 2018-04-11 22:44:12,705 : INFO : epoch 43: threshold: 0.044 avg_dist: 0.999 num_edges: 13936 2018-04-11 22:44:12,714 : INFO : epoch 44: threshold: 0.045 avg_dist: 0.999 num_edges: 13854 2018-04-11 22:44:12,721 : INFO : epoch 45: threshold: 0.046 avg_dist: 0.999 num_edges: 13779 2018-04-11 22:44:12,737 : INFO : epoch 46: threshold: 0.047 avg_dist: 0.998 num_edges: 13696 2018-04-11 22:44:12,748 : INFO : epoch 47: threshold: 0.048 avg_dist: 0.998 num_edges: 13619 2018-04-11 22:44:12,762 : INFO : epoch 48: threshold: 0.049 avg_dist: 0.998 num_edges: 13537 2018-04-11 22:44:12,774 : INFO : epoch 49: threshold: 0.050 avg_dist: 0.998 num_edges: 13454 2018-04-11 22:44:12,784 : INFO : epoch 50: threshold: 0.051 avg_dist: 0.998 num_edges: 13367 2018-04-11 22:44:12,796 : INFO : epoch 51: threshold: 0.052 avg_dist: 0.998 num_edges: 13292 2018-04-11 22:44:12,808 : INFO : epoch 52: threshold: 0.053 avg_dist: 0.998 num_edges: 13186 2018-04-11 22:44:12,821 : INFO : epoch 53: threshold: 0.054 avg_dist: 0.998 num_edges: 13097 2018-04-11 22:44:12,828 : INFO : epoch 54: threshold: 0.055 avg_dist: 0.998 num_edges: 13024 2018-04-11 22:44:12,836 : INFO : epoch 55: threshold: 0.056 avg_dist: 0.998 num_edges: 12941 2018-04-11 22:44:12,845 : INFO : epoch 56: threshold: 0.057 avg_dist: 0.998 num_edges: 12878 2018-04-11 22:44:12,857 : INFO : epoch 57: threshold: 0.058 avg_dist: 0.998 num_edges: 12802 2018-04-11 22:44:12,865 : INFO : epoch 58: threshold: 0.059 avg_dist: 0.998 num_edges: 12729 2018-04-11 22:44:12,872 : INFO : epoch 59: threshold: 0.060 avg_dist: 0.998 num_edges: 12650 2018-04-11 22:44:12,881 : INFO : epoch 60: threshold: 0.061 avg_dist: 0.998 num_edges: 12564 2018-04-11 22:44:12,894 : INFO : epoch 61: threshold: 0.062 avg_dist: 0.998 num_edges: 12486 2018-04-11 22:44:12,903 : INFO : epoch 62: threshold: 0.063 avg_dist: 0.998 num_edges: 12416 2018-04-11 22:44:12,913 : INFO : epoch 63: threshold: 0.064 avg_dist: 0.998 num_edges: 12338 2018-04-11 22:44:12,925 : INFO : epoch 64: threshold: 0.065 avg_dist: 0.998 num_edges: 12256 2018-04-11 22:44:12,933 : INFO : epoch 65: threshold: 0.066 avg_dist: 0.998 num_edges: 12173 2018-04-11 22:44:12,940 : INFO : epoch 66: threshold: 0.067 avg_dist: 0.998 num_edges: 12098 2018-04-11 22:44:12,950 : INFO : epoch 67: threshold: 0.068 avg_dist: 0.998 num_edges: 12030 2018-04-11 22:44:12,960 : INFO : epoch 68: threshold: 0.069 avg_dist: 0.998 num_edges: 11962 2018-04-11 22:44:12,968 : INFO : epoch 69: threshold: 0.070 avg_dist: 0.998 num_edges: 11886 2018-04-11 22:44:12,977 : INFO : epoch 70: threshold: 0.071 avg_dist: 0.998 num_edges: 11824 2018-04-11 22:44:12,985 : INFO : epoch 71: threshold: 0.072 avg_dist: 0.998 num_edges: 11761 2018-04-11 22:44:12,993 : INFO : epoch 72: threshold: 0.073 avg_dist: 0.998 num_edges: 11694 2018-04-11 22:44:13,005 : INFO : epoch 73: threshold: 0.074 avg_dist: 0.998 num_edges: 11630 2018-04-11 22:44:13,015 : INFO : epoch 74: threshold: 0.075 avg_dist: 0.998 num_edges: 11554 2018-04-11 22:44:13,027 : INFO : epoch 75: threshold: 0.076 avg_dist: 0.998 num_edges: 11481 2018-04-11 22:44:13,042 : INFO : epoch 76: threshold: 0.077 avg_dist: 0.998 num_edges: 11417 2018-04-11 22:44:13,051 : INFO : epoch 77: threshold: 0.078 avg_dist: 0.998 num_edges: 11354 2018-04-11 22:44:13,065 : INFO : epoch 78: threshold: 0.079 avg_dist: 0.998 num_edges: 11304 2018-04-11 22:44:13,075 : INFO : epoch 79: threshold: 0.080 avg_dist: 0.998 num_edges: 11236 2018-04-11 22:44:13,089 : INFO : epoch 80: threshold: 0.081 avg_dist: 0.998 num_edges: 11184 2018-04-11 22:44:13,102 : INFO : epoch 81: threshold: 0.082 avg_dist: 0.998 num_edges: 11117 2018-04-11 22:44:13,112 : INFO : epoch 82: threshold: 0.083 avg_dist: 0.998 num_edges: 11045 2018-04-11 22:44:13,119 : INFO : epoch 83: threshold: 0.084 avg_dist: 0.998 num_edges: 10987 2018-04-11 22:44:13,128 : INFO : epoch 84: threshold: 0.085 avg_dist: 0.998 num_edges: 10933 2018-04-11 22:44:13,137 : INFO : epoch 85: threshold: 0.086 avg_dist: 0.998 num_edges: 10874 2018-04-11 22:44:13,147 : INFO : epoch 86: threshold: 0.087 avg_dist: 0.998 num_edges: 10793 2018-04-11 22:44:13,157 : INFO : epoch 87: threshold: 0.088 avg_dist: 0.998 num_edges: 10723 2018-04-11 22:44:13,170 : INFO : epoch 88: threshold: 0.089 avg_dist: 0.998 num_edges: 10660 2018-04-11 22:44:13,179 : INFO : epoch 89: threshold: 0.090 avg_dist: 0.998 num_edges: 10594 2018-04-11 22:44:13,193 : INFO : epoch 90: threshold: 0.091 avg_dist: 0.998 num_edges: 10537 2018-04-11 22:44:13,206 : INFO : epoch 91: threshold: 0.092 avg_dist: 0.998 num_edges: 10474 2018-04-11 22:44:13,221 : INFO : epoch 92: threshold: 0.093 avg_dist: 0.998 num_edges: 10411 2018-04-11 22:44:13,235 : INFO : epoch 93: threshold: 0.094 avg_dist: 0.998 num_edges: 10362 2018-04-11 22:44:13,242 : INFO : epoch 94: threshold: 0.095 avg_dist: 0.998 num_edges: 10308 2018-04-11 22:44:13,253 : INFO : epoch 95: threshold: 0.096 avg_dist: 0.998 num_edges: 10256 2018-04-11 22:44:13,261 : INFO : epoch 96: threshold: 0.097 avg_dist: 0.998 num_edges: 10186 2018-04-11 22:44:13,271 : INFO : epoch 97: threshold: 0.098 avg_dist: 0.998 num_edges: 10133 2018-04-11 22:44:13,281 : INFO : epoch 98: threshold: 0.099 avg_dist: 0.998 num_edges: 10070 2018-04-11 22:44:13,292 : INFO : epoch 99: threshold: 0.100 avg_dist: 0.998 num_edges: 10012 2018-04-11 22:44:13,301 : INFO : epoch 100: threshold: 0.101 avg_dist: 0.998 num_edges: 9955 2018-04-11 22:44:13,310 : INFO : epoch 101: threshold: 0.102 avg_dist: 0.998 num_edges: 9915 2018-04-11 22:44:13,318 : INFO : epoch 102: threshold: 0.103 avg_dist: 0.998 num_edges: 9860 2018-04-11 22:44:13,327 : INFO : epoch 103: threshold: 0.104 avg_dist: 0.998 num_edges: 9801 2018-04-11 22:44:13,335 : INFO : epoch 104: threshold: 0.105 avg_dist: 0.998 num_edges: 9757 2018-04-11 22:44:13,348 : INFO : epoch 105: threshold: 0.106 avg_dist: 0.998 num_edges: 9712 2018-04-11 22:44:13,355 : INFO : epoch 106: threshold: 0.107 avg_dist: 0.998 num_edges: 9655 2018-04-11 22:44:13,364 : INFO : epoch 107: threshold: 0.108 avg_dist: 0.998 num_edges: 9608 2018-04-11 22:44:13,376 : INFO : epoch 108: threshold: 0.109 avg_dist: 0.998 num_edges: 9561 2018-04-11 22:44:13,384 : INFO : epoch 109: threshold: 0.110 avg_dist: 0.998 num_edges: 9518 2018-04-11 22:44:13,394 : INFO : epoch 110: threshold: 0.111 avg_dist: 0.998 num_edges: 9474 2018-04-11 22:44:13,404 : INFO : epoch 111: threshold: 0.112 avg_dist: 0.998 num_edges: 9425 2018-04-11 22:44:13,411 : INFO : epoch 112: threshold: 0.113 avg_dist: 0.998 num_edges: 9367 2018-04-11 22:44:13,420 : INFO : epoch 113: threshold: 0.114 avg_dist: 0.998 num_edges: 9322 2018-04-11 22:44:13,429 : INFO : epoch 114: threshold: 0.115 avg_dist: 0.998 num_edges: 9279 2018-04-11 22:44:13,441 : INFO : epoch 115: threshold: 0.116 avg_dist: 0.998 num_edges: 9225 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epoch 718: threshold: 0.719 avg_dist: 0.965 num_edges: 765 2018-04-11 22:44:18,393 : INFO : epoch 719: threshold: 0.720 avg_dist: 0.965 num_edges: 764 2018-04-11 22:44:18,404 : INFO : epoch 720: threshold: 0.721 avg_dist: 0.965 num_edges: 762 2018-04-11 22:44:18,411 : INFO : epoch 721: threshold: 0.722 avg_dist: 0.965 num_edges: 757 2018-04-11 22:44:18,419 : INFO : epoch 722: threshold: 0.723 avg_dist: 0.970 num_edges: 752 2018-04-11 22:44:18,430 : INFO : epoch 723: threshold: 0.724 avg_dist: 0.970 num_edges: 747 2018-04-11 22:44:18,443 : INFO : epoch 724: threshold: 0.725 avg_dist: 0.970 num_edges: 744 2018-04-11 22:44:18,451 : INFO : epoch 725: threshold: 0.726 avg_dist: 0.970 num_edges: 740 2018-04-11 22:44:18,458 : INFO : epoch 726: threshold: 0.727 avg_dist: 0.970 num_edges: 737 2018-04-11 22:44:18,468 : INFO : epoch 727: threshold: 0.728 avg_dist: 0.970 num_edges: 733 2018-04-11 22:44:18,476 : INFO : epoch 728: threshold: 0.729 avg_dist: 0.970 num_edges: 727 2018-04-11 22:44:18,485 : INFO : epoch 729: threshold: 0.730 avg_dist: 0.969 num_edges: 720 2018-04-11 22:44:18,492 : INFO : epoch 730: threshold: 0.731 avg_dist: 0.969 num_edges: 716 2018-04-11 22:44:18,502 : INFO : epoch 731: threshold: 0.732 avg_dist: 0.969 num_edges: 709 2018-04-11 22:44:18,508 : INFO : epoch 732: threshold: 0.733 avg_dist: 0.969 num_edges: 703 2018-04-11 22:44:18,517 : INFO : epoch 733: threshold: 0.734 avg_dist: 0.969 num_edges: 702 2018-04-11 22:44:18,524 : INFO : epoch 734: threshold: 0.735 avg_dist: 0.969 num_edges: 697 2018-04-11 22:44:18,536 : INFO : epoch 735: threshold: 0.736 avg_dist: 0.969 num_edges: 690 2018-04-11 22:44:18,544 : INFO : epoch 736: threshold: 0.737 avg_dist: 0.969 num_edges: 683 2018-04-11 22:44:18,551 : INFO : epoch 737: threshold: 0.738 avg_dist: 0.969 num_edges: 680 2018-04-11 22:44:18,558 : INFO : epoch 738: threshold: 0.739 avg_dist: 0.969 num_edges: 675 2018-04-11 22:44:18,569 : INFO : epoch 739: threshold: 0.740 avg_dist: 0.969 num_edges: 671 2018-04-11 22:44:18,576 : INFO : epoch 740: threshold: 0.741 avg_dist: 0.968 num_edges: 666 2018-04-11 22:44:18,586 : INFO : epoch 741: threshold: 0.742 avg_dist: 0.968 num_edges: 664 2018-04-11 22:44:18,594 : INFO : epoch 742: threshold: 0.743 avg_dist: 0.968 num_edges: 662 2018-04-11 22:44:18,601 : INFO : epoch 743: threshold: 0.744 avg_dist: 0.968 num_edges: 661 2018-04-11 22:44:18,608 : INFO : epoch 744: threshold: 0.745 avg_dist: 0.968 num_edges: 656 2018-04-11 22:44:18,619 : INFO : epoch 745: threshold: 0.746 avg_dist: 0.968 num_edges: 654 2018-04-11 22:44:18,628 : INFO : epoch 746: threshold: 0.747 avg_dist: 0.968 num_edges: 648 2018-04-11 22:44:18,637 : INFO : epoch 747: threshold: 0.748 avg_dist: 0.968 num_edges: 644 2018-04-11 22:44:18,645 : INFO : epoch 748: threshold: 0.749 avg_dist: 0.968 num_edges: 640 2018-04-11 22:44:18,653 : INFO : epoch 749: threshold: 0.750 avg_dist: 0.968 num_edges: 635 2018-04-11 22:44:18,666 : INFO : epoch 750: threshold: 0.751 avg_dist: 0.968 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epoch 783: threshold: 0.784 avg_dist: 0.967 num_edges: 513 2018-04-11 22:44:18,929 : INFO : epoch 784: threshold: 0.785 avg_dist: 0.966 num_edges: 509 2018-04-11 22:44:18,935 : INFO : epoch 785: threshold: 0.786 avg_dist: 0.968 num_edges: 502 2018-04-11 22:44:18,944 : INFO : epoch 786: threshold: 0.787 avg_dist: 0.968 num_edges: 498 2018-04-11 22:44:18,951 : INFO : epoch 787: threshold: 0.788 avg_dist: 0.968 num_edges: 492 2018-04-11 22:44:18,958 : INFO : epoch 788: threshold: 0.789 avg_dist: 0.968 num_edges: 487 2018-04-11 22:44:18,965 : INFO : epoch 789: threshold: 0.790 avg_dist: 0.968 num_edges: 484 2018-04-11 22:44:18,972 : INFO : epoch 790: threshold: 0.791 avg_dist: 0.968 num_edges: 481 2018-04-11 22:44:18,983 : INFO : epoch 791: threshold: 0.792 avg_dist: 0.968 num_edges: 479 2018-04-11 22:44:18,990 : INFO : epoch 792: threshold: 0.793 avg_dist: 0.968 num_edges: 474 2018-04-11 22:44:18,997 : INFO : epoch 793: threshold: 0.794 avg_dist: 0.968 num_edges: 472 2018-04-11 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2018-04-11 22:44:19,097 : INFO : epoch 805: threshold: 0.806 avg_dist: 0.972 num_edges: 434 2018-04-11 22:44:19,104 : INFO : epoch 806: threshold: 0.807 avg_dist: 0.972 num_edges: 429 2018-04-11 22:44:19,111 : INFO : epoch 807: threshold: 0.808 avg_dist: 0.972 num_edges: 428 2018-04-11 22:44:19,119 : INFO : epoch 808: threshold: 0.809 avg_dist: 0.972 num_edges: 424 2018-04-11 22:44:19,125 : INFO : epoch 809: threshold: 0.810 avg_dist: 0.972 num_edges: 422 2018-04-11 22:44:19,132 : INFO : epoch 810: threshold: 0.811 avg_dist: 0.972 num_edges: 417 2018-04-11 22:44:19,139 : INFO : epoch 811: threshold: 0.812 avg_dist: 0.976 num_edges: 415 2018-04-11 22:44:19,147 : INFO : epoch 812: threshold: 0.813 avg_dist: 0.976 num_edges: 412 2018-04-11 22:44:19,155 : INFO : epoch 813: threshold: 0.814 avg_dist: 0.976 num_edges: 410 2018-04-11 22:44:19,167 : INFO : epoch 814: threshold: 0.815 avg_dist: 0.979 num_edges: 406 2018-04-11 22:44:19,174 : INFO : epoch 815: threshold: 0.816 avg_dist: 0.979 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epoch 848: threshold: 0.849 avg_dist: 0.979 num_edges: 301 2018-04-11 22:44:19,449 : INFO : epoch 849: threshold: 0.850 avg_dist: 0.979 num_edges: 297 2018-04-11 22:44:19,461 : INFO : epoch 850: threshold: 0.851 avg_dist: 0.979 num_edges: 294 2018-04-11 22:44:19,468 : INFO : epoch 851: threshold: 0.852 avg_dist: 0.979 num_edges: 294 2018-04-11 22:44:19,480 : INFO : epoch 852: threshold: 0.853 avg_dist: 0.979 num_edges: 292 2018-04-11 22:44:19,492 : INFO : epoch 853: threshold: 0.854 avg_dist: 0.979 num_edges: 290 2018-04-11 22:44:19,503 : INFO : epoch 854: threshold: 0.855 avg_dist: 0.979 num_edges: 288 2018-04-11 22:44:19,510 : INFO : epoch 855: threshold: 0.856 avg_dist: 0.979 num_edges: 285 2018-04-11 22:44:19,518 : INFO : epoch 856: threshold: 0.857 avg_dist: 0.979 num_edges: 282 2018-04-11 22:44:19,525 : INFO : epoch 857: threshold: 0.858 avg_dist: 0.979 num_edges: 279 2018-04-11 22:44:19,537 : INFO : epoch 858: threshold: 0.859 avg_dist: 0.979 num_edges: 278 2018-04-11 22:44:19,544 : INFO : epoch 859: threshold: 0.860 avg_dist: 0.978 num_edges: 272 2018-04-11 22:44:19,553 : INFO : epoch 860: threshold: 0.861 avg_dist: 0.978 num_edges: 270 2018-04-11 22:44:19,560 : INFO : epoch 861: threshold: 0.862 avg_dist: 0.978 num_edges: 269 2018-04-11 22:44:19,567 : INFO : epoch 862: threshold: 0.863 avg_dist: 0.978 num_edges: 266 2018-04-11 22:44:19,575 : INFO : epoch 863: threshold: 0.864 avg_dist: 0.978 num_edges: 265 2018-04-11 22:44:19,583 : INFO : epoch 864: threshold: 0.865 avg_dist: 0.978 num_edges: 262 2018-04-11 22:44:19,592 : INFO : epoch 865: threshold: 0.866 avg_dist: 0.978 num_edges: 260 2018-04-11 22:44:19,599 : INFO : epoch 866: threshold: 0.867 avg_dist: 0.978 num_edges: 254 2018-04-11 22:44:19,607 : INFO : epoch 867: threshold: 0.868 avg_dist: 0.978 num_edges: 254 2018-04-11 22:44:19,615 : INFO : epoch 868: threshold: 0.869 avg_dist: 0.978 num_edges: 250 2018-04-11 22:44:19,625 : INFO : epoch 869: threshold: 0.870 avg_dist: 0.978 num_edges: 248 2018-04-11 22:44:19,634 : INFO : epoch 870: threshold: 0.871 avg_dist: 0.978 num_edges: 245 2018-04-11 22:44:19,649 : INFO : epoch 871: threshold: 0.872 avg_dist: 0.978 num_edges: 243 2018-04-11 22:44:19,665 : INFO : epoch 872: threshold: 0.873 avg_dist: 0.978 num_edges: 242 2018-04-11 22:44:19,671 : INFO : epoch 873: threshold: 0.874 avg_dist: 0.986 num_edges: 238 2018-04-11 22:44:19,679 : INFO : epoch 874: threshold: 0.875 avg_dist: 0.986 num_edges: 229 2018-04-11 22:44:19,687 : INFO : epoch 875: threshold: 0.876 avg_dist: 0.986 num_edges: 227 2018-04-11 22:44:19,693 : INFO : epoch 876: threshold: 0.877 avg_dist: 0.986 num_edges: 223 2018-04-11 22:44:19,702 : INFO : epoch 877: threshold: 0.878 avg_dist: 0.986 num_edges: 221 2018-04-11 22:44:19,708 : INFO : epoch 878: threshold: 0.879 avg_dist: 0.986 num_edges: 218 2018-04-11 22:44:19,717 : INFO : epoch 879: threshold: 0.880 avg_dist: 0.986 num_edges: 218 2018-04-11 22:44:19,731 : INFO : epoch 880: threshold: 0.881 avg_dist: 0.985 num_edges: 214 2018-04-11 22:44:19,742 : INFO : epoch 881: threshold: 0.882 avg_dist: 0.985 num_edges: 213 2018-04-11 22:44:19,750 : INFO : epoch 882: threshold: 0.883 avg_dist: 0.989 num_edges: 210 2018-04-11 22:44:19,762 : INFO : epoch 883: threshold: 0.884 avg_dist: 0.989 num_edges: 210 2018-04-11 22:44:19,769 : INFO : epoch 884: threshold: 0.885 avg_dist: 0.989 num_edges: 208 2018-04-11 22:44:19,782 : INFO : epoch 885: threshold: 0.886 avg_dist: 0.993 num_edges: 206 2018-04-11 22:44:19,791 : INFO : epoch 886: threshold: 0.887 avg_dist: 0.993 num_edges: 206 2018-04-11 22:44:19,805 : INFO : epoch 887: threshold: 0.888 avg_dist: 0.993 num_edges: 205 2018-04-11 22:44:19,811 : INFO : epoch 888: threshold: 0.889 avg_dist: 0.993 num_edges: 203 2018-04-11 22:44:19,819 : INFO : epoch 889: threshold: 0.890 avg_dist: 0.993 num_edges: 201 2018-04-11 22:44:19,826 : INFO : epoch 890: threshold: 0.891 avg_dist: 0.993 num_edges: 200 2018-04-11 22:44:19,837 : INFO : epoch 891: threshold: 0.892 avg_dist: 0.992 num_edges: 196 2018-04-11 22:44:19,845 : INFO : epoch 892: threshold: 0.893 avg_dist: 0.992 num_edges: 194 2018-04-11 22:44:19,852 : INFO : epoch 893: threshold: 0.894 avg_dist: 0.992 num_edges: 191 2018-04-11 22:44:19,858 : INFO : epoch 894: threshold: 0.895 avg_dist: 0.992 num_edges: 191 2018-04-11 22:44:19,864 : INFO : epoch 895: threshold: 0.896 avg_dist: 0.992 num_edges: 188 2018-04-11 22:44:19,872 : INFO : epoch 896: threshold: 0.897 avg_dist: 0.992 num_edges: 184 2018-04-11 22:44:19,880 : INFO : epoch 897: threshold: 0.898 avg_dist: 0.992 num_edges: 182 2018-04-11 22:44:19,888 : INFO : epoch 898: threshold: 0.899 avg_dist: 0.992 num_edges: 179 2018-04-11 22:44:19,895 : INFO : epoch 899: threshold: 0.900 avg_dist: 0.991 num_edges: 175 2018-04-11 22:44:19,902 : INFO : epoch 900: threshold: 0.901 avg_dist: 0.991 num_edges: 175 2018-04-11 22:44:19,909 : INFO : epoch 901: threshold: 0.902 avg_dist: 0.991 num_edges: 173 2018-04-11 22:44:19,916 : INFO : epoch 902: threshold: 0.903 avg_dist: 0.991 num_edges: 172 2018-04-11 22:44:19,922 : INFO : epoch 903: threshold: 0.904 avg_dist: 0.991 num_edges: 167 2018-04-11 22:44:19,929 : INFO : epoch 904: threshold: 0.905 avg_dist: 0.991 num_edges: 166 2018-04-11 22:44:19,936 : INFO : epoch 905: threshold: 0.906 avg_dist: 0.991 num_edges: 165 2018-04-11 22:44:19,943 : INFO : epoch 906: threshold: 0.907 avg_dist: 0.991 num_edges: 164 2018-04-11 22:44:19,949 : INFO : epoch 907: threshold: 0.908 avg_dist: 0.991 num_edges: 161 2018-04-11 22:44:19,956 : INFO : epoch 908: threshold: 0.909 avg_dist: 0.991 num_edges: 160 2018-04-11 22:44:19,963 : INFO : epoch 909: threshold: 0.910 avg_dist: 0.991 num_edges: 159 2018-04-11 22:44:19,969 : INFO : epoch 910: threshold: 0.911 avg_dist: 0.991 num_edges: 157 2018-04-11 22:44:19,976 : INFO : epoch 911: threshold: 0.912 avg_dist: 0.991 num_edges: 155 2018-04-11 22:44:19,984 : INFO : epoch 912: threshold: 0.913 avg_dist: 0.990 num_edges: 152 2018-04-11 22:44:19,991 : INFO : epoch 913: threshold: 0.914 avg_dist: 0.990 num_edges: 146 2018-04-11 22:44:19,997 : INFO : epoch 914: threshold: 0.915 avg_dist: 0.990 num_edges: 142 2018-04-11 22:44:20,004 : INFO : epoch 915: threshold: 0.916 avg_dist: 0.990 num_edges: 138 2018-04-11 22:44:20,015 : INFO : epoch 916: threshold: 0.917 avg_dist: 0.990 num_edges: 137 2018-04-11 22:44:20,027 : INFO : epoch 917: threshold: 0.918 avg_dist: 0.990 num_edges: 136 2018-04-11 22:44:20,035 : INFO : epoch 918: threshold: 0.919 avg_dist: 0.990 num_edges: 134 2018-04-11 22:44:20,047 : INFO : epoch 919: threshold: 0.920 avg_dist: 0.990 num_edges: 131 2018-04-11 22:44:20,059 : INFO : epoch 920: threshold: 0.921 avg_dist: 0.990 num_edges: 129 2018-04-11 22:44:20,068 : INFO : epoch 921: threshold: 0.922 avg_dist: 0.990 num_edges: 124 2018-04-11 22:44:20,075 : INFO : epoch 922: threshold: 0.923 avg_dist: 0.990 num_edges: 123 2018-04-11 22:44:20,087 : INFO : epoch 923: threshold: 0.924 avg_dist: 0.990 num_edges: 122 2018-04-11 22:44:20,094 : INFO : epoch 924: threshold: 0.925 avg_dist: 0.990 num_edges: 120 2018-04-11 22:44:20,101 : INFO : epoch 925: threshold: 0.926 avg_dist: 0.990 num_edges: 119 2018-04-11 22:44:20,107 : INFO : epoch 926: threshold: 0.927 avg_dist: 0.990 num_edges: 117 2018-04-11 22:44:20,114 : INFO : epoch 927: threshold: 0.928 avg_dist: 0.990 num_edges: 115 2018-04-11 22:44:20,120 : INFO : epoch 928: threshold: 0.929 avg_dist: 0.990 num_edges: 114 2018-04-11 22:44:20,127 : INFO : epoch 929: threshold: 0.930 avg_dist: 0.990 num_edges: 112 2018-04-11 22:44:20,140 : INFO : epoch 930: threshold: 0.931 avg_dist: 0.989 num_edges: 109 2018-04-11 22:44:20,152 : INFO : epoch 931: threshold: 0.932 avg_dist: 0.989 num_edges: 108 2018-04-11 22:44:20,160 : INFO : epoch 932: threshold: 0.933 avg_dist: 0.989 num_edges: 107 2018-04-11 22:44:20,169 : INFO : epoch 933: threshold: 0.934 avg_dist: 0.988 num_edges: 106 2018-04-11 22:44:20,176 : INFO : epoch 934: threshold: 0.935 avg_dist: 0.988 num_edges: 104 2018-04-11 22:44:20,184 : INFO : epoch 935: threshold: 0.936 avg_dist: 0.988 num_edges: 103 2018-04-11 22:44:20,192 : INFO : epoch 936: threshold: 0.937 avg_dist: 0.988 num_edges: 100 2018-04-11 22:44:20,198 : INFO : epoch 937: threshold: 0.938 avg_dist: 0.988 num_edges: 96 2018-04-11 22:44:20,206 : INFO : epoch 938: threshold: 0.939 avg_dist: 0.988 num_edges: 94 2018-04-11 22:44:20,214 : INFO : epoch 939: threshold: 0.940 avg_dist: 0.988 num_edges: 91 2018-04-11 22:44:20,223 : INFO : epoch 940: threshold: 0.941 avg_dist: 0.988 num_edges: 90 2018-04-11 22:44:20,232 : INFO : epoch 941: threshold: 0.942 avg_dist: 0.988 num_edges: 85 2018-04-11 22:44:20,240 : INFO : epoch 942: threshold: 0.943 avg_dist: 0.988 num_edges: 81 2018-04-11 22:44:20,252 : INFO : epoch 943: threshold: 0.944 avg_dist: 0.988 num_edges: 80 2018-04-11 22:44:20,259 : INFO : epoch 944: threshold: 0.945 avg_dist: 0.992 num_edges: 76 2018-04-11 22:44:20,267 : INFO : epoch 945: threshold: 0.946 avg_dist: 0.992 num_edges: 74 2018-04-11 22:44:20,275 : INFO : epoch 946: threshold: 0.947 avg_dist: 0.992 num_edges: 71 2018-04-11 22:44:20,287 : INFO : epoch 947: threshold: 0.948 avg_dist: 0.992 num_edges: 68 2018-04-11 22:44:20,293 : INFO : epoch 948: threshold: 0.949 avg_dist: 0.992 num_edges: 66 2018-04-11 22:44:20,300 : INFO : epoch 949: threshold: 0.950 avg_dist: 0.992 num_edges: 65 2018-04-11 22:44:20,307 : INFO : epoch 950: threshold: 0.951 avg_dist: 0.992 num_edges: 64 2018-04-11 22:44:20,314 : INFO : epoch 951: threshold: 0.952 avg_dist: 0.995 num_edges: 63 2018-04-11 22:44:20,320 : INFO : epoch 952: threshold: 0.953 avg_dist: 0.995 num_edges: 63 2018-04-11 22:44:20,330 : INFO : epoch 953: threshold: 0.954 avg_dist: 0.995 num_edges: 61 2018-04-11 22:44:20,338 : INFO : epoch 954: threshold: 0.955 avg_dist: 0.995 num_edges: 58 2018-04-11 22:44:20,345 : INFO : epoch 955: threshold: 0.956 avg_dist: 0.998 num_edges: 54 2018-04-11 22:44:20,352 : INFO : epoch 956: threshold: 0.957 avg_dist: 0.998 num_edges: 54 2018-04-11 22:44:20,358 : INFO : epoch 957: threshold: 0.958 avg_dist: 1.000 num_edges: 48 2018-04-11 22:44:20,366 : INFO : epoch 958: threshold: 0.959 avg_dist: 1.000 num_edges: 47 2018-04-11 22:44:20,374 : INFO : epoch 959: threshold: 0.960 avg_dist: 1.000 num_edges: 44 2018-04-11 22:44:20,382 : INFO : epoch 960: threshold: 0.961 avg_dist: 1.000 num_edges: 43 2018-04-11 22:44:20,389 : INFO : epoch 961: threshold: 0.962 avg_dist: 1.000 num_edges: 40 2018-04-11 22:44:20,395 : INFO : epoch 962: threshold: 0.963 avg_dist: 1.000 num_edges: 40 2018-04-11 22:44:20,403 : INFO : epoch 963: threshold: 0.964 avg_dist: 1.000 num_edges: 38 2018-04-11 22:44:20,412 : INFO : epoch 964: threshold: 0.965 avg_dist: 1.000 num_edges: 36 2018-04-11 22:44:20,419 : INFO : epoch 965: threshold: 0.966 avg_dist: 1.000 num_edges: 31 2018-04-11 22:44:20,426 : INFO : epoch 966: threshold: 0.967 avg_dist: 1.000 num_edges: 31 2018-04-11 22:44:20,433 : INFO : epoch 967: threshold: 0.968 avg_dist: 1.000 num_edges: 31 2018-04-11 22:44:20,439 : INFO : epoch 968: threshold: 0.969 avg_dist: 1.000 num_edges: 31 2018-04-11 22:44:20,446 : INFO : epoch 969: threshold: 0.970 avg_dist: 1.000 num_edges: 30 2018-04-11 22:44:20,453 : INFO : epoch 970: threshold: 0.971 avg_dist: 1.000 num_edges: 29 2018-04-11 22:44:20,463 : INFO : epoch 971: threshold: 0.972 avg_dist: 1.000 num_edges: 29 2018-04-11 22:44:20,473 : INFO : epoch 972: threshold: 0.973 avg_dist: 1.000 num_edges: 28 2018-04-11 22:44:20,481 : INFO : epoch 973: threshold: 0.974 avg_dist: 1.000 num_edges: 28 2018-04-11 22:44:20,487 : INFO : epoch 974: threshold: 0.975 avg_dist: 1.000 num_edges: 26 2018-04-11 22:44:20,494 : INFO : epoch 975: threshold: 0.976 avg_dist: 1.000 num_edges: 24 2018-04-11 22:44:20,501 : INFO : epoch 976: threshold: 0.977 avg_dist: 1.000 num_edges: 21 2018-04-11 22:44:20,511 : INFO : epoch 977: threshold: 0.978 avg_dist: 1.000 num_edges: 20 2018-04-11 22:44:20,523 : INFO : epoch 978: threshold: 0.979 avg_dist: 1.000 num_edges: 19 2018-04-11 22:44:20,529 : INFO : epoch 979: threshold: 0.980 avg_dist: 1.000 num_edges: 17 2018-04-11 22:44:20,536 : INFO : epoch 980: threshold: 0.981 avg_dist: 1.000 num_edges: 17 2018-04-11 22:44:20,542 : INFO : epoch 981: threshold: 0.982 avg_dist: 1.000 num_edges: 16 2018-04-11 22:44:20,549 : INFO : epoch 982: threshold: 0.983 avg_dist: 1.000 num_edges: 15 2018-04-11 22:44:20,556 : INFO : epoch 983: threshold: 0.984 avg_dist: 1.000 num_edges: 12 2018-04-11 22:44:20,564 : INFO : epoch 984: threshold: 0.985 avg_dist: 1.000 num_edges: 12 2018-04-11 22:44:20,572 : INFO : epoch 985: threshold: 0.986 avg_dist: 1.000 num_edges: 12 2018-04-11 22:44:20,581 : INFO : epoch 986: threshold: 0.987 avg_dist: 1.000 num_edges: 10 2018-04-11 22:44:20,588 : INFO : epoch 987: threshold: 0.988 avg_dist: 1.000 num_edges: 8 2018-04-11 22:44:20,597 : INFO : epoch 988: threshold: 0.989 avg_dist: 1.000 num_edges: 7 2018-04-11 22:44:20,605 : INFO : epoch 989: threshold: 0.990 avg_dist: 1.000 num_edges: 5 2018-04-11 22:44:20,613 : INFO : epoch 990: threshold: 0.991 avg_dist: 1.000 num_edges: 5 2018-04-11 22:44:20,625 : INFO : epoch 991: threshold: 0.992 avg_dist: 1.000 num_edges: 5 2018-04-11 22:44:20,632 : INFO : epoch 992: threshold: 0.993 avg_dist: 1.000 num_edges: 4 2018-04-11 22:44:20,639 : INFO : epoch 993: threshold: 0.994 avg_dist: 1.000 num_edges: 3 2018-04-11 22:44:20,652 : INFO : epoch 994: threshold: 0.995 avg_dist: 1.000 num_edges: 1 2018-04-11 22:44:20,660 : INFO : epoch 995: threshold: 0.996 avg_dist: 1.000 num_edges: 1 2018-04-11 22:44:20,670 : INFO : epoch 996: threshold: 0.997 avg_dist: 1.000 num_edges: 1 2018-04-11 22:44:20,678 : INFO : epoch 997: threshold: 0.998 avg_dist: 1.000 num_edges: 1 2018-04-11 22:44:20,687 : INFO : epoch 998: threshold: 0.999 avg_dist: 1.000 num_edges: 1
threshold: 0.832 dist 0.965399 (721, 0.722) edges 361/361 (831)
threshold: 0.832 dist 0.965210 (721, 0.722) edges 361/360 (831)
In [106]:
# Set the edges:
topicnet_adj_mat = set_edges(cosim, best_threshold, binary=False) # the edges are the cosine similarities!
# topicnet_adj_mat.dump('./res/topicnetref6yr_adjmat.pkl')
2018-04-11 22:44:20,695 : INFO : dimension: 184, 184
Visualise¶
Thresholding:¶
In [ ]:
f = threshold_plot(
thresholds,
distances,
edges,
best_threshold,
closest_edges,
best_j_dist_threshold,
lowest_avg_distances,
adj_mat, cosim
)
In [ ]:
f.savefig(
'./IMG/topicnetref6yrw_thresholding.png', format='png', bbox_inches='tight')
As series of matrices:¶
In [108]:
f = plt.figure(figsize=(15, 5))
# ORIGINAL COLALB NET
ax1 = f.add_subplot(131)
ax1.axis('off')
sns.heatmap(adj_mat, cbar=False, square=True)
ax1.set_title('infnet6yrw\n(#Edges={})'.format(int(np.sum(adj_mat) // 2)))
# COSIM
ax2 = f.add_subplot(132)
ax2.axis('off')
sns.heatmap(
cosim,
cbar=False,
square=True,
ax=ax2,
vmax=1,
vmin=0,
robust=True,
cmap='gnuplot2')
ax2.set_title('Cosine similairty\nbetween researchers')
# FINAL :
ax3 = f.add_subplot(133)
ax3.axis('off')
sns.heatmap(topicnet_adj_mat, cbar=False, square=True, ax=ax3)
ax3.set_title('topicnetref6yrw\n($\epsilon$={:.3f}, #Edges={})'.format(
best_threshold,
int(np.sum(topicnet_adj_mat) // 2)))
f.tight_layout()
In [ ]:
f.savefig('./IMG/topicnetref6yr_adjmats.png')
As networks:¶
In [109]:
g_topicnetref6yrw = adj_mat_to_graph(topicnet_adj_mat, collabnet.id.tolist())
In [110]:
print(nx.info(g_topicnetref6yrw))
Name: Type: Graph Number of nodes: 119 Number of edges: 361 Average degree: 6.0672
In [111]:
artist.draw_default_layout(
g_topicnetref6yrw,
lookup_poinf=get_lookup_poinf(),
file_prefix='topicnetref6yrw',
with_weight=True,scale=2,
SAVE_GRAPHS=False)
2018-04-11 22:45:03,640 : INFO : SAVE_GRAPHS: False
In [112]:
artist.draw_circular_layout(
g_topicnetref6yrw,
get_lookup_poinf(),
file_prefix='topicnetref6yrw',
with_weight=True,
SAVE_GRAPHS=False)
2018-04-11 22:45:06,890 : INFO : SAVE_GRAPHS: False
Descriptive Statistics¶
In [114]:
_x = nx.average_clustering(g_topicnetref6yrw, count_zeros=False, weight='weight')
print(_x)
_dict = nx.clustering(g_topicnetref6yrw, weight='weight')
print((np.mean(list(_dict.values()))))
0.6997657843413585 0.4821915488738774
In [115]:
print(nx.transitivity(g_topicnetref6yrw))
0.7741935483870968
In [116]:
x = nx.degree_histogram(g_topicnetref6yrw)
print(x[0]+x[1])
32